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Sahid MA, Babar MUH, Uddin MP. Predictive modeling of multi-class diabetes mellitus using machine learning and filtering iraqi diabetes data dynamics. PLoS One 2024; 19:e0300785. [PMID: 38753669 PMCID: PMC11098411 DOI: 10.1371/journal.pone.0300785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/05/2024] [Indexed: 05/18/2024] Open
Abstract
Diabetes is a persistent metabolic disorder linked to elevated levels of blood glucose, commonly referred to as blood sugar. This condition can have detrimental effects on the heart, blood vessels, eyes, kidneys, and nerves as time passes. It is a chronic ailment that arises when the body fails to produce enough insulin or is unable to effectively use the insulin it produces. When diabetes is not properly managed, it often leads to hyperglycemia, a condition characterized by elevated blood sugar levels or impaired glucose tolerance. This can result in significant harm to various body systems, including the nerves and blood vessels. In this paper, we propose a multiclass diabetes mellitus detection and classification approach using an extremely imbalanced Laboratory of Medical City Hospital data dynamics. We also formulate a new dataset that is moderately imbalanced based on the Laboratory of Medical City Hospital data dynamics. To correctly identify the multiclass diabetes mellitus, we employ three machine learning classifiers namely support vector machine, logistic regression, and k-nearest neighbor. We also focus on dimensionality reduction (feature selection-filter, wrapper, and embedded method) to prune the unnecessary features and to scale up the classification performance. To optimize the classification performance of classifiers, we tune the model by hyperparameter optimization with 10-fold grid search cross-validation. In the case of the original extremely imbalanced dataset with 70:30 partition and support vector machine classifier, we achieved maximum accuracy of 0.964, precision of 0.968, recall of 0.964, F1-score of 0.962, Cohen kappa of 0.835, and AUC of 0.99 by using top 4 feature according to filter method. By using the top 9 features according to wrapper-based sequential feature selection, the k-nearest neighbor provides an accuracy of 0.935 and 1.0 for the other performance metrics. For our created moderately imbalanced dataset with an 80:20 partition, the SVM classifier achieves a maximum accuracy of 0.938, and 1.0 for other performance metrics. For the multiclass diabetes mellitus detection and classification, our experiments outperformed conducted research based on the Laboratory of Medical City Hospital data dynamics.
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Affiliation(s)
- Md Abdus Sahid
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Mozaddid Ul Hoque Babar
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
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2
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Chowdhury AH, Rad D, Rahman MS. Predicting anxiety, depression, and insomnia among Bangladeshi university students using tree-based machine learning models. Health Sci Rep 2024; 7:e2037. [PMID: 38650723 PMCID: PMC11033350 DOI: 10.1002/hsr2.2037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/21/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024] Open
Abstract
Background and Aims Mental health problem is a rising public health concern. People of all ages, specially Bangladeshi university students, are more affected by this burden. Thus, the objective of the study was to use tree-based machine learning (ML) models to identify major risk factors and predict anxiety, depression, and insomnia in university students. Methods A social media-based cross-sectional survey was employed for data collection. We used Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index (ISI-7) scale for measuring students' anxiety, depression and insomnia problems. The tree-based supervised decision tree (DT), random forest (RF) and robust eXtreme Gradient Boosting (XGBoost) ML algorithms were used to build the prediction models and their predictive performance was evaluated using confusion matrix and receiver operating characteristic (ROC) curves. Results Of the 1250 students surveyed, 64.7% were male and 35.3% were female. The students' ages ranged from 18 to 26 years old, with an average age of 22.24 years (SD = 1.30). Majority of the students (72.6%) were from rural areas and social media addicted (56.6%). Almost 83.3% of the students had moderate to severe anxiety, 84.7% had moderate to severe depression and 76.5% had moderate to severe insomnia problems. Students' social media addiction, age, academic performance, smoking status, monthly family income and morningness-eveningness are the main risk factors of anxiety, depression and insomnia. The highest predictive performance was observed from the XGBoost model for anxiety, depression and insomnia. Conclusion The study findings offer valuable insights for stakeholders, families and policymakers enabling a more profound comprehension of the pressing mental health disorders. This understanding can guide the formulation of improved policy strategies, initiatives for mental health promotion, and the development of effective counseling services within university campus. Additionally, our proposed model might play a critical role in diagnosing and predicting mental health problems among Bangladeshi university students and similar settings.
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Affiliation(s)
| | - Dana Rad
- Center of Research Development and Innovation in PsychologyAurel Vlaicu University of AradAradRomania
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3
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Wu Y, Chen W, Zhao Y, Gu M, Gao Y, Ke Y, Wang L, Wang M, Zhang W, Chen Y, Huo W, Fu X, Li X, Zhang D, Qin P, Hu F, Liu Y, Sun X, Zhang M, Hu D. Visit to visit transition in TXNIP gene methylation and the risk of type 2 diabetes mellitus: a nested case-control study. J Hum Genet 2024:10.1038/s10038-024-01243-8. [PMID: 38528048 DOI: 10.1038/s10038-024-01243-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 02/27/2024] [Accepted: 03/10/2024] [Indexed: 03/27/2024]
Abstract
Our study aimed to investigate the association between the transition of the TXNIP gene methylation level and the risk of incident type 2 diabetes mellitus (T2DM). This study included 263 incident cases of T2DM and 263 matched non-T2DM participants. According to the methylation levels of five loci (CpG1-5; chr1:145441102-145442001) on the TXNIP gene, the participants were classified into four transition groups: maintained low, low to high, high to low, and maintained high methylation levels. Compared with individuals whose methylation level of CpG2-5 at the TXNIP gene was maintained low, individuals with maintained high methylation levels showed a 61-87% reduction in T2DM risk (66% for CpG2 [OR: 0.34, 95% CI: 0.14, 0.80]; 77% for CpG3 [OR: 0.23, 95% CI: 0.07, 0.78]; 87% for CpG4 [OR: 0.13, 95% CI: 0.03, 0.56]; and 61% for CpG5 [OR: 0.39, 95% CI: 0.16, 0.92]). Maintained high methylation levels of four loci of the TXNIP gene are associated with a reduction of T2DM incident risk in the current study. Our study suggests that preserving hypermethylation levels of the TXNIP gene may hold promise as a potential preventive measure against the onset of T2DM.
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Affiliation(s)
- Yuying Wu
- Department of General Practice, Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Weiling Chen
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Yang Zhao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Minqi Gu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Yajuan Gao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yamin Ke
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Longkang Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Mengmeng Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Wenkai Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Yaobing Chen
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Weifeng Huo
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xueru Fu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Xi Li
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - Dongdong Zhang
- Department of General Practice, Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Pei Qin
- Department of Medical Record Management, Shenzhen Qianbai Shekou Free Trade Zone Hospital, Shenzhen, Guangdong, People's Republic of China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Yu Liu
- Department of General Practice, Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Xizhuo Sun
- Department of General Practice, Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Ming Zhang
- Department of General Practice, Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China
| | - Dongsheng Hu
- Department of General Practice, Affiliated Luohu Hospital of Shenzhen University Medical School, Shenzhen, Guangdong, People's Republic of China.
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Reza MS, Amin R, Yasmin R, Kulsum W, Ruhi S. Improving diabetes disease patients classification using stacking ensemble method with PIMA and local healthcare data. Heliyon 2024; 10:e24536. [PMID: 38312584 PMCID: PMC10834804 DOI: 10.1016/j.heliyon.2024.e24536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 01/06/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024] Open
Abstract
Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %-12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry.
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Affiliation(s)
- Md Shamim Reza
- Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
| | - Ruhul Amin
- Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
| | - Rubia Yasmin
- Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
| | - Woomme Kulsum
- Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
| | - Sabba Ruhi
- Department of Statistics, Pabna University of Science and Technology, Pabna, 6600, Bangladesh
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5
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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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6
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Feng X, Cai Y, Xin R. Optimizing diabetes classification with a machine learning-based framework. BMC Bioinformatics 2023; 24:428. [PMID: 37957549 PMCID: PMC10644638 DOI: 10.1186/s12859-023-05467-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 09/04/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Diabetes is a metabolic disorder usually caused by insufficient secretion of insulin from the pancreas or insensitivity of cells to insulin, resulting in long-term elevated blood sugar levels in patients. Patients usually present with frequent urination, thirst, and hunger. If left untreated, it can lead to various complications that can affect essential organs and even endanger life. Therefore, developing an intelligent diagnosis framework for diabetes is necessary. RESULT This paper proposes a machine learning-based diabetes classification framework machine learning optimized GAN. The framework encompasses several methodological approaches to address the diverse challenges encountered during the analysis. These approaches encompass the implementation of the mean and median joint filling method for handling missing values, the application of the cap method for outlier processing, and the utilization of SMOTEENN to mitigate sample imbalance. Additionally, the framework incorporates the employment of the proposed Diabetes Classification Model based on Generative Adversarial Network and employs logistic regression for detailed feature analysis. The effectiveness of the framework is evaluated using both the PIMA dataset and the diabetes dataset obtained from the GEO database. The experimental findings showcase our model achieved exceptional results, including a binary classification accuracy of 96.27%, tertiary classification accuracy of 99.31%, precision and f1 score of 0.9698, recall of 0.9698, and an AUC of 0.9702. CONCLUSION The experimental results show that the framework proposed in this paper can accurately classify diabetes and provide new ideas for intelligent diagnosis of diabetes.
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Affiliation(s)
- Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin, 130000, People's Republic of China
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, 130012, People's Republic of China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, 130012, People's Republic of China
| | - Yihuai Cai
- School of Science, Jilin Institute of Chemical Technology, Jilin, 130000, People's Republic of China.
| | - Ruihao Xin
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 130000, People's Republic of China.
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, People's Republic of China.
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7
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Al-Maini M, Maindarkar M, Kitas GD, Khanna NN, Misra DP, Johri AM, Mantella L, Agarwal V, Sharma A, Singh IM, Tsoulfas G, Laird JR, Faa G, Teji J, Turk M, Viskovic K, Ruzsa Z, Mavrogeni S, Rathore V, Miner M, Kalra MK, Isenovic ER, Saba L, Fouda MM, Suri JS. Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review. Rheumatol Int 2023; 43:1965-1982. [PMID: 37648884 DOI: 10.1007/s00296-023-05415-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Abstract
The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.
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Affiliation(s)
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, L4Z 4C4, Canada
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
- Asia Pacific Vascular Society, New Delhi, 110001, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13 9PL, UK
| | - Narendra N Khanna
- Asia Pacific Vascular Society, New Delhi, 110001, India
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, 110001, India
| | | | - Amer M Johri
- Division of Cardiology, Department of Medicine, Queen's University, Kingston, Canada
| | - Laura Mantella
- Division of Cardiology, Department of Medicine, University of Toronto, Toronto, Canada
| | - Vikas Agarwal
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Aman Sharma
- Department of Immunology, SGPIMS, Lucknow, 226014, India
| | - Inder M Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124, Thessaloniki, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, 94574, USA
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124, Cagliari, Italy
| | - Jagjit Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753, Delmenhorst, Germany
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, UHID, 10 000, Zagreb, Croatia
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, Athens, Greece
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, 95823, USA
| | - Martin Miner
- Men's Health Centre, Miriam Hospital Providence, Providence, RI, 02906, USA
| | - Manudeep K Kalra
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of the Republic of Serbia, University of Belgrade, 11000, Belgrade, Serbia
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138, Cagliari, Italy
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, 83209, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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8
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Chellappan D, Rajaguru H. Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data. Biomimetics (Basel) 2023; 8:503. [PMID: 37887634 PMCID: PMC10604158 DOI: 10.3390/biomimetics8060503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/08/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023] Open
Abstract
In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods.
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Affiliation(s)
- Dinesh Chellappan
- Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641 407, Tamil Nadu, India;
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638 401, Tamil Nadu, India
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9
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Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
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Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Chellappan D, Rajaguru H. Detection of Diabetes through Microarray Genes with Enhancement of Classifiers Performance. Diagnostics (Basel) 2023; 13:2654. [PMID: 37627916 PMCID: PMC10453776 DOI: 10.3390/diagnostics13162654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023] Open
Abstract
Diabetes is a life-threatening, non-communicable disease. Diabetes mellitus is a prevalent chronic disease with a significant global impact. The timely detection of diabetes in patients is necessary for an effective treatment. The primary objective of this study is to propose a novel approach for identifying type II diabetes mellitus using microarray gene data. Specifically, our research focuses on the performance enhancement of methods for detecting diabetes. Four different Dimensionality Reduction techniques, Detrend Fluctuation Analysis (DFA), the Chi-square probability density function (Chi2pdf), the Firefly algorithm, and Cuckoo Search, are used to reduce high dimensional data. Metaheuristic algorithms like Particle Swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Seven classifiers, Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), and Support Vector Machine-Radial Basis Function (SVM-RBF), are utilized to classify the diabetic and non-diabetic classes. The classifiers' performances are analyzed through parameters such as accuracy, recall, precision, F1 score, error rate, Matthews Correlation Coefficient (MCC), Jaccard metric, and kappa. The SVM (RBF) classifier with the Chi2pdf Dimensionality Reduction technique with a PSO feature selection method attained a high accuracy of 91% with a Kappa of 0.7961, outperforming all of the other classifiers.
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Affiliation(s)
- Dinesh Chellappan
- Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641 407, Tamil Nadu, India
| | - Harikumar Rajaguru
- Department of Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638 401, Tamil Nadu, India;
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11
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Iparraguirre-Villanueva O, Espinola-Linares K, Flores Castañeda RO, Cabanillas-Carbonell M. Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes. Diagnostics (Basel) 2023; 13:2383. [PMID: 37510127 PMCID: PMC10378239 DOI: 10.3390/diagnostics13142383] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 06/23/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.
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12
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Nilashi M, Abumalloh RA, Alyami S, Alghamdi A, Alrizq M. A Combined Method for Diabetes Mellitus Diagnosis Using Deep Learning, Singular Value Decomposition, and Self-Organizing Map Approaches. Diagnostics (Basel) 2023; 13:diagnostics13101821. [PMID: 37238305 DOI: 10.3390/diagnostics13101821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/10/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
Diabetes in humans is a rapidly expanding chronic disease and a major crisis in modern societies. The classification of diabetics is a challenging and important procedure that allows the interpretation of diabetic data and diagnosis. Missing values in datasets can impact the prediction accuracy of the methods for the diagnosis. Due to this, a variety of machine learning techniques has been studied in the past. This research has developed a new method using machine learning techniques for diabetes risk prediction. The method was developed through the use of clustering and prediction learning techniques. The method uses Singular Value Decomposition for missing value predictions, a Self-Organizing Map for clustering the data, STEPDISC for feature selection, and an ensemble of Deep Belief Network classifiers for diabetes mellitus prediction. The performance of the proposed method is compared with the previous prediction methods developed by machine learning techniques. The results reveal that the deployed method can accurately predict diabetes mellitus for a set of real-world datasets.
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Affiliation(s)
- Mehrbakhsh Nilashi
- UCSI Graduate Business School, UCSI University, No. 1 Jalan Menara Gading, UCSI Heights, Cheras, Kuala Lumpur 56000, Malaysia
- Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia (USM), George Town 11800, Malaysia
| | - Rabab Ali Abumalloh
- Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
| | - Sultan Alyami
- Computer Science Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
| | - Abdullah Alghamdi
- Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
| | - Mesfer Alrizq
- Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
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13
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Othman NA, Azhar MAAS, Damanhuri NS, Mahadi IA, Abbas MH, Shamsuddin SA, Chase JG. Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107566. [PMID: 37186981 DOI: 10.1016/j.cmpb.2023.107566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 04/09/2023] [Accepted: 04/21/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The identification of insulinaemic pharmacokinetic parameters using the least-squares criterion approach is easily influenced by outlying data due to its sensitivity. Furthermore, the least-squares criterion has a tendency to overfit and produce incorrect results. Hence, this research proposes an alternative approach using the artificial neural network (ANN) with two hidden layers to optimize the identifying of insulinaemic pharmacokinetic parameters. The ANN is selected for its ability to avoid overfitting parameters and its faster speed in processing data. METHODS 18 voluntarily participants were recruited from the Canterbury and Otago region of New Zealand to take part in a Dynamic Insulin Sensitivity and Secretion Test (DISST) clinical trial. A total of 46 DISST data were collected. However, due to ambiguous and inconsistency, 4 data had to be removed. Analysis was done using MATLAB 2020a. RESULTS AND DISCUSSION Results show that, with 42 gathered dataset, the ANN generates higher gains, ∅P = 20.73 [12.21, 28.57] mU·L·mmol-1·min-1 and ∅D = 60.42 [26.85, 131.38] mU·L·mmol-1 as compared to the linear least square method, ∅P = 19.67 [11.81, 28.02] mU·L·mmol-1 ·min-1 and ∅D = 46.21 [7.25, 116.71] mU·L·mmol-1. The average value of the insulin sensitivity (SI) of ANN is lower with, SI = 16 × 10-4 L·mU-1 ·min-1 than the linear least square, SI = 17 × 10-4 L·mU-1 ·min-1. CONCLUSION Although the ANN analysis provided a lower SI value, the results were more dependable than the linear least square model because the ANN approach yielded a better model fitting accuracy than the linear least square method with a lower residual error of less than 5%. With the implementation of this ANN architecture, it shows that ANN able to produce minimal error during optimization process particularly when dealing with outlying data. The findings may provide extra information to clinicians, allowing them to gain a better knowledge of the heterogenous aetiology of diabetes and therapeutic intervention options.
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Affiliation(s)
- Nor Azlan Othman
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
| | - Muhammad Amirul Aizad Shaharul Azhar
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Nor Salwa Damanhuri
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Iqmal Ammar Mahadi
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Mohd Hussaini Abbas
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Sarah Addyani Shamsuddin
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
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14
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Mondal HS, Ahmed KA, Birbilis N, Hossain MZ. Machine learning for detecting DNA attachment on SPR biosensor. Sci Rep 2023; 13:3742. [PMID: 36879019 PMCID: PMC9987359 DOI: 10.1038/s41598-023-29395-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 02/03/2023] [Indexed: 03/08/2023] Open
Abstract
Optoelectric biosensors measure the conformational changes of biomolecules and their molecular interactions, allowing researchers to use them in different biomedical diagnostics and analysis activities. Among different biosensors, surface plasmon resonance (SPR)-based biosensors utilize label-free and gold-based plasmonic principles with high precision and accuracy, allowing these gold-based biosensors as one of the preferred methods. The dataset generated from these biosensors are being used in different machine learning (ML) models for disease diagnosis and prognosis, but there is a scarcity of models to develop or assess the accuracy of SPR-based biosensors and ensure a reliable dataset for downstream model development. Current study proposed innovative ML-based DNA detection and classification models from the reflective light angles on different gold surfaces of biosensors and associated properties. We have conducted several statistical analyses and different visualization techniques to evaluate the SPR-based dataset and applied t-SNE feature extraction and min-max normalization to differentiate classifiers of low-variances. We experimented with several ML classifiers, namely support vector machine (SVM), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbors (KNN), logistic regression (LR) and random forest (RF) and evaluated our findings in terms of different evaluation metrics. Our analysis showed the best accuracy of 0.94 by RF, DT and KNN for DNA classification and 0.96 by RF and KNN for DNA detection tasks. Considering area under the receiver operating characteristic curve (AUC) (0.97), precision (0.96) and F1-score (0.97), we found RF performed best for both tasks. Our research shows the potentiality of ML models in the field of biosensor development, which can be expanded to develop novel disease diagnosis and prognosis tools in the future.
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Affiliation(s)
- Himadri Shekhar Mondal
- ANU College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT, 2600, Australia. .,Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT, 2601, Australia.
| | - Khandaker Asif Ahmed
- Australian Centre for Disease Preparedness (ACDP), CSIRO, Geelong, VIC, 3220, Australia
| | - Nick Birbilis
- ANU College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT, 2600, Australia.,Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC, 3125, Australia
| | - Md Zakir Hossain
- ANU College of Engineering, Computing and Cybernetics, The Australian National University, Canberra, ACT, 2600, Australia. .,Biological Data Science Institute, The Australian National University, Canberra, ACT, 2600, Australia. .,Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT, 2601, Australia. .,Faculty of Science and Engineering, Curtin University, Perth, WA, 6102, Australia.
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15
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Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi MZ. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetol Metab Syndr 2022; 14:196. [PMID: 36572938 PMCID: PMC9793536 DOI: 10.1186/s13098-022-00969-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 12/16/2022] [Indexed: 12/28/2022] Open
Abstract
Diabetes as a metabolic illness can be characterized by increased amounts of blood glucose. This abnormal increase can lead to critical detriment to the other organs such as the kidneys, eyes, heart, nerves, and blood vessels. Therefore, its prediction, prognosis, and management are essential to prevent harmful effects and also recommend more useful treatments. For these goals, machine learning algorithms have found considerable attention and have been developed successfully. This review surveys the recently proposed machine learning (ML) and deep learning (DL) models for the objectives mentioned earlier. The reported results disclose that the ML and DL algorithms are promising approaches for controlling blood glucose and diabetes. However, they should be improved and employed in large datasets to affirm their applicability.
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16
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Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel) 2022; 10:healthcare10122493. [PMID: 36554017 PMCID: PMC9777836 DOI: 10.3390/healthcare10122493] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 12/03/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.
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Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study. J Clin Med 2022; 11:jcm11226844. [PMID: 36431321 PMCID: PMC9693632 DOI: 10.3390/jcm11226844] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/15/2022] [Accepted: 11/16/2022] [Indexed: 11/22/2022] Open
Abstract
A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.
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18
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Islam MM, Rahman MJ, Menhazul Abedin M, Ahammed B, Ali M, Ahmed NF, Maniruzzaman M. Identification of the risk factors of type 2 diabetes and its prediction using machine learning techniques. Health Syst (Basingstoke) 2022; 12:243-254. [PMID: 37234468 PMCID: PMC10208154 DOI: 10.1080/20476965.2022.2141141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 10/20/2022] [Indexed: 11/07/2022] Open
Abstract
This study identified the risk factors for type 2 diabetes (T2D) and proposed a machine learning (ML) technique for predicting T2D. The risk factors for T2D were identified by multiple logistic regression (MLR) using p-value (p<0.05). Then, five ML-based techniques, including logistic regression, naïve Bayes, J48, multilayer perceptron, and random forest (RF) were employed to predict T2D. This study utilized two publicly available datasets, derived from the National Health and Nutrition Examination Survey, 2009-2010 and 2011-2012. About 4922 respondents with 387 T2D patients were included in 2009-2010 dataset, whereas 4936 respondents with 373 T2D patients were included in 2011-2012. This study identified six risk factors (age, education, marital status, SBP, smoking, and BMI) for 2009-2010 and nine risk factors (age, race, marital status, SBP, DBP, direct cholesterol, physical activity, smoking, and BMI) for 2011-2012. RF-based classifier obtained 95.9% accuracy, 95.7% sensitivity, 95.3% F-measure, and 0.946 area under the curve.
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Affiliation(s)
- Md. Merajul Islam
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
- Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh, Bangladesh
| | | | | | - Benojir Ahammed
- Statistics Discipline, Khulna University, Khulna, Bangladesh
| | - Mohammad Ali
- Statistics Discipline, Khulna University, Khulna, Bangladesh
| | - N.A.M Faisal Ahmed
- Institute of Education and Research, University of Rajshahi, Rajshahi, Bangladesh
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Johri AM, Singh KV, Mantella LE, Saba L, Sharma A, Laird JR, Utkarsh K, Singh IM, Gupta S, Kalra MS, Suri JS. Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Comput Biol Med 2022; 150:106018. [PMID: 36174330 DOI: 10.1016/j.compbiomed.2022.106018] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 08/06/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system. METHODS Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked. RESULTS The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s. CONCLUSION DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.
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Affiliation(s)
- Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | | | - Laura E Mantella
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | | | | | - Suneet Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Manudeep S Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA; Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA.
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20
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Predictive Analysis of Diabetes-Risk with Class Imbalance. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3078025. [PMID: 36268149 PMCID: PMC9578843 DOI: 10.1155/2022/3078025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Diabetes type 2 (T2DM) is a common chronic disease, increasingly leading to many complications and affecting vital organs. Hyperglycemia is the main characteristic caused by insufficient insulin secretion and poses a serious risk to human health. The objective is to construct a type-2 diabetes prediction model with high classification accuracy. Advanced machine learning and predictive model techniques are utilized to achieve cutting-edge techniques for the early diagnosis of diabetes. This paper proposes an efficient performance model to predict and classify the minority class of type-2 diabetes. The impact of oversampling and undersampling approaches to reduce the effect of an unbalanced class has been compared to classification performance algorithms. Synthetic Minority Oversampling (SMOTE) and Tomek-links techniques are applied and examined. The outcomes were then compared to the original unbalanced dataset using an artificial neural network (ANN) predictive model. The model is compared with other state-of-the-art classifiers such as support vector machine (SVM), random forest (RF), and decision tree (DT). The tuned model had the best accuracy of 92.2%. The experimental findings clearly manifest the improvement in accuracy and evaluation metrics in terms of AUC and F1-measure using the SMOTE oversampling strategy rather than the baseline and undersampling schemes. The study recommends adopting dynamic hyperparameter optimization to further improve accuracy.
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21
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Khan M, Khurshid M, Vatsa M, Singh R, Duggal M, Singh K. On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review. Front Public Health 2022; 10:880034. [PMID: 36249249 PMCID: PMC9562034 DOI: 10.3389/fpubh.2022.880034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 05/30/2022] [Indexed: 01/21/2023] Open
Abstract
A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.
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Affiliation(s)
- Misaal Khan
- Department of Smart Healthcare, Indian Institute of Technology Jodhpur, Karwar, India,All India Institute of Medical Sciences Jodhpur, Jodhpur, India
| | - Mahapara Khurshid
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mayank Vatsa
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India,*Correspondence: Mayank Vatsa
| | - Richa Singh
- Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, India
| | - Mona Duggal
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Kuldeep Singh
- Department of Pediatrics, All India Institute of Medical Sciences Jodhpur, Jodhpur, India
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22
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Dutta A, Hasan MK, Ahmad M, Awal MA, Islam MA, Masud M, Meshref H. Early Prediction of Diabetes Using an Ensemble of Machine Learning Models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912378. [PMID: 36231678 PMCID: PMC9566114 DOI: 10.3390/ijerph191912378] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/20/2022] [Accepted: 09/24/2022] [Indexed: 05/15/2023]
Abstract
Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.
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Affiliation(s)
- Aishwariya Dutta
- Department of Biomedical Engineering (BME), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
- Department of Biomedical Engineering (BME), Military Institute of Science and Technology (MIST), Mirpur Cantonment, Dhaka 1216, Bangladesh
| | - Md. Kamrul Hasan
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Mohiuddin Ahmad
- Department of Electrical and Electronic Engineering (EEE), Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
| | - Md. Abdul Awal
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
- Electronics and Communication Engineering (ECE) Discipline, Khulna University (KU), Khulna 9208, Bangladesh
- Correspondence:
| | | | - Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Hossam Meshref
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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23
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Cheheltani R, King N, Lee S, North B, Kovarik D, Evans-Molina C, Leavitt N, Dutta S. Predicting misdiagnosed adult-onset type 1 diabetes using machine learning. Diabetes Res Clin Pract 2022; 191:110029. [PMID: 35940302 PMCID: PMC10631495 DOI: 10.1016/j.diabres.2022.110029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 07/29/2022] [Accepted: 08/01/2022] [Indexed: 11/27/2022]
Abstract
AIMS It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes. METHODS In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history. RESULTS The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR. CONCLUSIONS This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.
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Affiliation(s)
- Rabee Cheheltani
- Predictive Analytics, Real World Solutions, IQVIA, Wayne, PA, USA
| | - Nicholas King
- Predictive Analytics, Real World Solutions, IQVIA, Wayne, PA, USA
| | - Suyin Lee
- Predictive Analytics, Real World Solutions, IQVIA, Wayne, PA, USA
| | - Benjamin North
- Predictive Analytics, Real World Solutions, IQVIA, Wayne, PA, USA
| | | | - Carmella Evans-Molina
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Nadejda Leavitt
- Predictive Analytics, Real World Solutions, IQVIA, Wayne, PA, USA
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24
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Jena B, Saxena S, Nayak GK, Balestrieri A, Gupta N, Khanna NN, Laird JR, Kalra MK, Fouda MM, Saba L, Suri JS. Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework. Cancers (Basel) 2022; 14:4052. [PMID: 36011048 PMCID: PMC9406706 DOI: 10.3390/cancers14164052] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/17/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.
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Affiliation(s)
- Biswajit Jena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | - Gopal Krishna Nayak
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India
| | | | - Neha Gupta
- Department of IT, Bharati Vidyapeeth’s College of Engineering, New Delhi 110056, India
| | - Narinder N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Luca Saba
- Department of Radiology, AOU, University of Cagliari, 09124 Cagliari, Italy
| | - Jasjit S. Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
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25
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Suri JS, Agarwal S, Saba L, Chabert GL, Carriero A, Paschè A, Danna P, Mehmedović A, Faa G, Jujaray T, Singh IM, Khanna NN, Laird JR, Sfikakis PP, Agarwal V, Teji JS, R Yadav R, Nagy F, Kincses ZT, Ruzsa Z, Viskovic K, Kalra MK. Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation. J Med Syst 2022; 46:62. [PMID: 35988110 PMCID: PMC9392994 DOI: 10.1007/s10916-022-01850-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, “COVLIAS 1.0-Unseen” proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations—two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.
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26
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Khanna NN, Maindarkar M, Puvvula A, Paul S, Bhagawati M, Ahluwalia P, Ruzsa Z, Sharma A, Munjral S, Kolluri R, Krishnan PR, Singh IM, Laird JR, Fatemi M, Alizad A, Dhanjil SK, Saba L, Balestrieri A, Faa G, Paraskevas KI, Misra DP, Agarwal V, Sharma A, Teji J, Al-Maini M, Nicolaides A, Rathore V, Naidu S, Liblik K, Johri AM, Turk M, Sobel DW, Pareek G, Miner M, Viskovic K, Tsoulfas G, Protogerou AD, Mavrogeni S, Kitas GD, Fouda MM, Kalra MK, Suri JS. Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report. J Cardiovasc Dev Dis 2022; 9:jcdd9080268. [PMID: 36005433 PMCID: PMC9409845 DOI: 10.3390/jcdd9080268] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 07/30/2022] [Accepted: 08/09/2022] [Indexed: 12/15/2022] Open
Abstract
The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India
| | - Mahesh Maindarkar
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Annu’s Hospitals for Skin and Diabetes, Nellore 524101, India
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, Faculty of Medicine, University of Szeged, 6720 Szeged, Hungary
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Smiksha Munjral
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Raghu Kolluri
- Ohio Health Heart and Vascular, Columbus, OH 43214, USA
| | | | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Surinder K. Dhanjil
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy
| | - Antonella Balestrieri
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria, 09124 Cagliari, Italy
| | | | - Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Aman Sharma
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
| | - Jagjit Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, 2408 Nicosia, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA 95119, USA
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
| | - Kiera Liblik
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
| | - David W. Sobel
- Rheumatology Unit, National Kapodistrian University of Athens, 15772 Athens, Greece
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
| | - George Tsoulfas
- Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece
| | - Athanasios D. Protogerou
- Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 17674 Athens, Greece
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Manudeep K. Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-916-749-5628
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27
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Agarwal M, Agarwal S, Saba L, Chabert GL, Gupta S, Carriero A, Pasche A, Danna P, Mehmedovic A, Faa G, Shrivastava S, Jain K, Jain H, Jujaray T, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Sobel DW, Miner M, Balestrieri A, Sfikakis PP, Tsoulfas G, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Yadav RR, Nagy F, Kincses ZT, Ruzsa Z, Naidu S, Viskovic K, Kalra MK, Suri JS. Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Comput Biol Med 2022; 146:105571. [PMID: 35751196 PMCID: PMC9123805 DOI: 10.1016/j.compbiomed.2022.105571] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/05/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. METHOD ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. RESULTS Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. CONCLUSIONS Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.
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Affiliation(s)
- Mohit Agarwal
- Department of Computer Science Engineering, Bennett University, India
| | - Sushant Agarwal
- Department of Computer Science Engineering, PSIT, Kanpur, India; Advanced Knowledge Engineering Centre, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Gian Luca Chabert
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Suneet Gupta
- Department of Computer Science Engineering, Bennett University, India
| | - Alessandro Carriero
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Alessio Pasche
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | - Pietro Danna
- Depart of Radiology, "Maggiore della Carità" Hospital, University of Piemonte Orientale, Via Solaroli 17, 28100, Novara, Italy
| | | | - Gavino Faa
- Department of Pathology - AOU of Cagliari, Italy
| | - Saurabh Shrivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Kanishka Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Harsh Jain
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India
| | - Tanay Jujaray
- Dept of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, CA, USA
| | | | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | - Amer M Johri
- Division of Cardiology, Queen's University, Kingston, Ontario, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - David W Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George Tsoulfas
- Aristoteleion University of Thessaloniki, Thessaloniki, Greece
| | | | | | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK; Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, USA
| | - Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, Canada
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and Univ. of Nicosia Medical School, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - Mostafa Fatemi
- Dept. of Physiology & Biomedical Engg., Mayo Clinic College of Medicine and Science, MN, USA
| | - Azra Alizad
- Dept. of Radiology, Mayo Clinic College of Medicine and Science, MN, USA
| | | | | | - Frence Nagy
- Department of Radiology, University of Szeged, 6725, Hungary
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN, USA
| | | | - Manudeep K Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jasjit S Suri
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad, 244001, India; Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA.
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Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12071543. [PMID: 35885449 PMCID: PMC9324237 DOI: 10.3390/diagnostics12071543] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 06/14/2022] [Accepted: 06/16/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.
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Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine. Cancers (Basel) 2022; 14:cancers14122860. [PMID: 35740526 PMCID: PMC9220825 DOI: 10.3390/cancers14122860] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Recently, radiogenomics has played a significant role and offered a new understanding of cancer’s biology and behavior in response to standard therapy. It also provides a more precise prognosis, investigation, and analysis of the patient’s cancer. Over the years, Artificial Intelligence (AI) has provided a significant strength in radiogenomics. In this paper, we offer computational and oncological prospects of the role of AI in radiogenomics, as well as its offers, achievements, opportunities, and limitations in the current clinical practices. Abstract Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.
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Olisah CC, Smith L, Smith M. Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106773. [PMID: 35429810 DOI: 10.1016/j.cmpb.2022.106773] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/25/2022] [Accepted: 03/22/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Diabetes mellitus is a metabolic disorder characterized by hyperglycemia, which results from the inadequacy of the body to secrete and respond to insulin. If not properly managed or diagnosed on time, diabetes can pose a risk to vital body organs such as the eyes, kidneys, nerves, heart, and blood vessels and so can be life-threatening. The many years of research in computational diagnosis of diabetes have pointed to machine learning to as a viable solution for the prediction of diabetes. However, the accuracy rate to date suggests that there is still much room for improvement. In this paper, we are proposing a machine learning framework for diabetes prediction and diagnosis using the PIMA Indian dataset and the laboratory of the Medical City Hospital (LMCH) diabetes dataset. We hypothesize that adopting feature selection and missing value imputation methods can scale up the performance of classification models in diabetes prediction and diagnosis. METHODS In this paper, a robust framework for building a diabetes prediction model to aid in the clinical diagnosis of diabetes is proposed. The framework includes the adoption of Spearman correlation and polynomial regression for feature selection and missing value imputation, respectively, from a perspective that strengthens their performances. Further, different supervised machine learning models, the random forest (RF) model, support vector machine (SVM) model, and our designed twice-growth deep neural network (2GDNN) model are proposed for classification. The models are optimized by tuning the hyperparameters of the models using grid search and repeated stratified k-fold cross-validation and evaluated for their ability to scale to the prediction problem. RESULTS Through experiments on the PIMA Indian and LMCH diabetes datasets, precision, sensitivity, F1-score, train-accuracy, and test-accuracy scores of 97.34%, 97.24%, 97.26%, 99.01%, 97.25 and 97.28%, 97.33%, 97.27%, 99.57%, 97.33, are achieved with the proposed 2GDNN model, respectively. CONCLUSION The data preprocessing approaches and the classifiers with hyperparameter optimization proposed within the machine learning framework yield a robust machine learning model that outperforms state-of-the-art results in diabetes mellitus prediction and diagnosis. The source code for the models of the proposed machine learning framework has been made publicly available.
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Affiliation(s)
- Chollette C Olisah
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK.
| | - Lyndon Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK
| | - Melvyn Smith
- Centre for Machine Vision, Bristol Robotics Laboratory, University of the West of England, Bristol, UK
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Liu Q, Zhang M, He Y, Zhang L, Zou J, Yan Y, Guo Y. Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques. J Pers Med 2022; 12:jpm12060905. [PMID: 35743691 PMCID: PMC9224915 DOI: 10.3390/jpm12060905] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/21/2022] [Accepted: 05/27/2022] [Indexed: 02/04/2023] Open
Abstract
Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.
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Affiliation(s)
- Qing Liu
- Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China; (Q.L.); (M.Z.)
| | - Miao Zhang
- Department of Epidemiology, School of Public Health, Wuhan University, Wuhan 430071, China; (Q.L.); (M.Z.)
| | - Yifeng He
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; (Y.H.); (J.Z.)
| | - Lei Zhang
- School of Mathematics and Statistics, Wuhan University, Wuhan 430070, China;
| | - Jingui Zou
- School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; (Y.H.); (J.Z.)
| | - Yaqiong Yan
- Wuhan Center for Disease Control and Prevention, Wuhan 430015, China;
| | - Yan Guo
- Wuhan Center for Disease Control and Prevention, Wuhan 430015, China;
- Correspondence:
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Khanna NN, Maindarkar M, Saxena A, Ahluwalia P, Paul S, Srivastava SK, Cuadrado-Godia E, Sharma A, Omerzu T, Saba L, Mavrogeni S, Turk M, Laird JR, Kitas GD, Fatemi M, Barqawi AB, Miner M, Singh IM, Johri A, Kalra MM, Agarwal V, Paraskevas KI, Teji JS, Fouda MM, Pareek G, Suri JS. Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review. Diagnostics (Basel) 2022; 12:1249. [PMID: 35626404 PMCID: PMC9141739 DOI: 10.3390/diagnostics12051249] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/14/2022] [Accepted: 05/15/2022] [Indexed: 12/12/2022] Open
Abstract
PURPOSE The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.
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Affiliation(s)
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Mahesh Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (M.M.); (S.P.)
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
| | - Ajit Saxena
- Department of Urology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - Puneet Ahluwalia
- Max Institute of Cancer Care, Max Super Specialty Hospital, New Delhi 110017, India;
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (M.M.); (S.P.)
| | - Saurabh K. Srivastava
- College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad 244001, India;
| | - Elisa Cuadrado-Godia
- Department of Neurology, Hospital del Mar Medical Research Institute, 08003 Barcelona, Spain;
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, USA;
| | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (T.O.); (M.T.)
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09124 Cagliari, Italy;
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Centre, 176 74 Athens, Greece;
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (T.O.); (M.T.)
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK;
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK
| | - Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, NY 55905, USA;
| | - Al Baha Barqawi
- Division of Urology, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Martin Miner
- Men’s Health Centre, Miriam Hospital Providence, Providence, RI 02906, USA;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
| | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | | | - Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
| | - Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA;
| | - Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA 95661, USA;
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Rajesh N., Irudayasamy A, Mohideen MSK, Ranjith CP. Classification of Vital Genetic Syndromes Associated With Diabetes Using ANN-Based CapsNet Approach. INTERNATIONAL JOURNAL OF E-COLLABORATION 2022. [DOI: 10.4018/ijec.307133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diabetes has been linked to a wide range of genetic abnormalities or disorders like Cushing syndrome, Wolfram’s syndrome. The factual significance of these relatively uncommon disorders originates from the knowledge that supplies into the potential processes driving prevalent diabetes. Diabetes-related syndromes are presently classified based on clinical and biochemical characteristics. However, until now, no expertise classification strategies are developed for classifying diabetes-associated syndrome disorders efficiently and accurately. Thus, we introduce an Artificial Neural Network framework based on CapsNets to categorize vital genetic disorders related to diabetes. Here, a capsule represents a bundle or set of neurons used to retain data about an essential subject and provides precise information in each image. The suggested approach was systematically compared using cutting-edge methods and basic classification models. With an overall 91.4 percent accuracy, the proposed CapsNets-based method provides the best sensitivity89.93%, specificity 90.77%, and F1-score value 93.10%
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Affiliation(s)
- Rajesh N.
- University of Technology and Applied Science, Shinas, Oman
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A Comprehensive Review of Various Diabetic Prediction Models: A Literature Survey. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8100697. [PMID: 35449835 PMCID: PMC9018179 DOI: 10.1155/2022/8100697] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/24/2022] [Accepted: 03/02/2022] [Indexed: 12/19/2022]
Abstract
Diabetes is a chronic disease characterized by a high amount of glucose in the blood and can cause too many complications also in the body, such as internal organ failure, retinopathy, and neuropathy. According to the predictions made by WHO, the figure may reach approximately 642 million by 2040, which means one in a ten may suffer from diabetes due to unhealthy lifestyle and lack of exercise. Many authors in the past have researched extensively on diabetes prediction through machine learning algorithms. The idea that had motivated us to present a review of various diabetic prediction models is to address the diabetic prediction problem by identifying, critically evaluating, and integrating the findings of all relevant, high-quality individual studies. In this paper, we have analysed the work done by various authors for diabetes prediction methods. Our analysis on diabetic prediction models was to find out the methods so as to select the best quality researches and to synthesize the different researches. Analysis of diabetes data disease is quite challenging because most of the data in the medical field are nonlinear, nonnormal, correlation structured, and complex in nature. Machine learning-based algorithms have been ruled out in the field of healthcare and medical imaging. Diabetes mellitus prediction at an early stage requires a different approach from other approaches. Machine learning-based system risk stratification can be used to categorize the patients into diabetic and controls. We strongly recommend our study because it comprises articles from various sources that will help other researchers on various diabetic prediction models.
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A Novel Approach for Feature Selection and Classification of Diabetes Mellitus: Machine Learning Methods. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3820360. [PMID: 35463255 PMCID: PMC9033325 DOI: 10.1155/2022/3820360] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/12/2022] [Accepted: 03/19/2022] [Indexed: 01/12/2023]
Abstract
An active research area where the experts from the medical field are trying to envisage the problem with more accuracy is diabetes prediction. Surveys conducted by WHO have shown a remarkable increase in the diabetic patients. Diabetes generally remains in dormant mode and it boosts the other diseases if patients are diagnosed with some other disease such as damage to the kidney vessels, problems in retina of the eye, and cardiac problem; if unidentified, it can create metabolic disorders and too many complications in the body. The main objective of our study is to draw a comparative study of different classifiers and feature selection methods to predict the diabetes with greater accuracy. In this paper, we have studied multilayer perceptron, decision trees, K-nearest neighbour, and random forest classifiers and few feature selection techniques were applied on the classifiers to detect the diabetes at an early stage. Raw data is subjected to preprocessing techniques, thus removing outliers and imputing missing values by mean and then in the end hyperparameters optimization. Experiments were conducted on PIMA Indians diabetes dataset using Weka 3.9 and the accuracy achieved for multilayer perceptron is 77.60%, for decision trees is 76.07%, for K-nearest neighbour is 78.58%, and for random forest is 79.8%, which is by far the best accuracy for random forest classifier.
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Swislocki AL. Glucose Trajectory: More than Changing Glucose Tolerance with Age? Metab Syndr Relat Disord 2022; 20:313-320. [DOI: 10.1089/met.2021.0093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Affiliation(s)
- Arthur L.M. Swislocki
- Medical Service, VA Northern California Health Care System (612/111), Martinez, California, USA
- Division of Endocrinology and Metabolism, Department of Internal Medicine, UC Davis School of Medicine, Sacramento, California, USA
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Suri JS, Paul S, Maindarkar MA, Puvvula A, Saxena S, Saba L, Turk M, Laird JR, Khanna NN, Viskovic K, Singh IM, Kalra M, Krishnan PR, Johri A, Paraskevas KI. Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. Metabolites 2022; 12:metabo12040312. [PMID: 35448500 PMCID: PMC9033076 DOI: 10.3390/metabo12040312] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022] Open
Abstract
Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.A.M.)
| | - Maheshrao A. Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.A.M.)
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
- Annu’s Hospitals for Skin & Diabetes, Gudur 524101, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751003, India;
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09121 Cagliari, Italy;
| | - Monika Turk
- Deparment of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India;
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
| | - Mannudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA;
| | | | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
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38
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Suri JS, Bhagawati M, Paul S, Protogeron A, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Paraskevas KI, Laird JR, Johri AM, Saba L, Kalra M. Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. Comput Biol Med 2022; 142:105204. [DOI: 10.1016/j.compbiomed.2021.105204] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/29/2021] [Accepted: 12/29/2021] [Indexed: 02/09/2023]
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Ilari L, Piersanti A, Göbl C, Burattini L, Kautzky-Willer A, Tura A, Morettini M. Unraveling the Factors Determining Development of Type 2 Diabetes in Women With a History of Gestational Diabetes Mellitus Through Machine-Learning Techniques. Front Physiol 2022; 13:789219. [PMID: 35250610 PMCID: PMC8892139 DOI: 10.3389/fphys.2022.789219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/11/2022] [Indexed: 11/13/2022] Open
Abstract
Gestational diabetes mellitus (GDM) is a type of diabetes that usually resolves at the end of the pregnancy but exposes to a higher risk of developing type 2 diabetes mellitus (T2DM). This study aimed to unravel the factors, among those that quantify specific metabolic processes, which determine progression to T2DM by using machine-learning techniques. Classification of women who did progress to T2DM (labeled as PROG, n = 19) vs. those who did not (labeled as NON-PROG, n = 59) progress to T2DM has been performed by using Orange software through a data analysis procedure on a generated data set including anthropometric data and a total of 34 features, extracted through mathematical modeling/methods procedures. Feature selection has been performed through decision tree algorithm and then Naïve Bayes and penalized (L2) logistic regression were used to evaluate the ability of the selected features to solve the classification problem. Performance has been evaluated in terms of area under the operating receiver characteristics (AUC), classification accuracy (CA), precision, sensitivity, specificity, and F1. Feature selection provided six features, and based on them, classification was performed as follows: AUC of 0.795, 0.831, and 0.884; CA of 0.827, 0.813, and 0.840; precision of 0.830, 0.854, and 0.834; sensitivity of 0.827, 0.813, and 0.840; specificity of 0.700, 0.821, and 0.662; and F1 of 0.828, 0.824, and 0.836 for tree algorithm, Naïve Bayes, and penalized logistic regression, respectively. Fasting glucose, age, and body mass index together with features describing insulin action and secretion may predict the development of T2DM in women with a history of GDM.
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Affiliation(s)
- Ludovica Ilari
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Agnese Piersanti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Alexandra Kautzky-Willer
- Division of Endocrinology and Metabolism, Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria
| | - Andrea Tura
- Metabolic Unit, CNR Institute of Neuroscience, Padua, Italy
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
- *Correspondence: Micaela Morettini,
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Arya M, Sastry G H, Motwani A, Kumar S, Zaguia A. A Novel Extra Tree Ensemble Optimized DL Framework (ETEODL) for Early Detection of Diabetes. Front Public Health 2022; 9:797877. [PMID: 35242738 PMCID: PMC8885585 DOI: 10.3389/fpubh.2021.797877] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 11/16/2021] [Indexed: 01/16/2023] Open
Abstract
Diabetes has been recognized as a global medical problem for more than half a century. Patients with diabetes can benefit from the Internet of Things (IoT) devices such as continuous glucose monitoring (CGM), intelligent pens, and similar devices. Smart devices generate continuous data streams that must be processed in real-time to benefit the users. The amount of medical data collected is vast and heterogeneous since it is gathered from various sources. An accurate diagnosis can be achieved through a variety of scientific and medical techniques. It is necessary to process this streaming data faster to obtain relevant and significant knowledge. Recently, the research has concentrated on improving the prediction model's performance by using ensemble-based and Deep Learning (DL) approaches. However, the performance of the DL model can degrade due to overfitting. This paper proposes the Extra-Tree Ensemble feature selection technique to reduce the input feature space with DL (ETEODL), a predictive framework to predict the likelihood of diabetes. In the proposed work, dropout layers follow the hidden layers of the DL model to prevent overfitting. This research utilized a dataset from the UCI Machine learning (ML) repository for an Early-stage prediction of diabetes. The proposed scheme results have been compared with state-of-the-art ML algorithms, and the comparison validates the effectiveness of the predictive framework. This proposed work, which outperforms the other selected classifiers, achieves a 97.38 per cent accuracy rate. F1-Score, precision, and recall percent are 96, 97.7, and 97.7, respectively. The comparison unveils the superiority of the suggested approach. Thus, the proposed method effectively improves the performance against the earlier ML techniques and recent DL approaches and avoids overfitting.
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Affiliation(s)
- Monika Arya
- Department of Computer Science and Engineering, Bhilai Institute of Technology, Durg, India
| | - Hanumat Sastry G
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
- *Correspondence: Hanumat Sastry G
| | - Anand Motwani
- School of Computing Science and Engineering, VIT Bhopal University, Sehore, India
| | - Sunil Kumar
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
| | - Atef Zaguia
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
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Konstantonis G, Singh KV, Sfikakis PP, Jamthikar AD, Kitas GD, Gupta SK, Saba L, Verrou K, Khanna NN, Ruzsa Z, Sharma AM, Laird JR, Johri AM, Kalra M, Protogerou A, Suri JS. Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 2022; 42:215-239. [PMID: 35013839 DOI: 10.1007/s00296-021-05062-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/29/2021] [Indexed: 12/31/2022]
Abstract
The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.
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Affiliation(s)
- George Konstantonis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Ankush D Jamthikar
- Research Scientist, AtheroPoint™, USA, Roseville, CA, USA.,Visvesvaraya National Institute of Technology, Nagpur, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK.,Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13, UK
| | - Suneet K Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Kleio Verrou
- Department of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Manudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Athanasios Protogerou
- Cardiovascular Prevention Unit, Department of Pathophysiology, National Kapodistrian University of Athens, Athens, Greece
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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Paul S, Maindarkar M, Saxena S, Saba L, Turk M, Kalra M, Krishnan PR, Suri JS. Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review. Diagnostics (Basel) 2022; 12:166. [PMID: 35054333 PMCID: PMC8774851 DOI: 10.3390/diagnostics12010166] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 12/27/2021] [Accepted: 01/01/2022] [Indexed: 12/13/2022] Open
Abstract
Background and Motivation: Diagnosis of Parkinson's disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. METHOD The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. RESULT The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were "deep learning with sketches as outcomes" and "machine learning with Electroencephalography," respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. CONCLUSION The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.
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Affiliation(s)
- Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Maheshrao Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751003, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09121 Cagliari, Italy
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia
| | - Manudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA
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43
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Classification and pattern extraction of incidents: a deep learning-based approach. Neural Comput Appl 2022. [DOI: 10.1007/s00521-021-06780-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
AbstractClassifying or predicting occupational incidents using both structured and unstructured (text) data are an unexplored area of research. Unstructured texts, i.e., incident narratives are often unutilized or underutilized. Besides the explicit information, there exist a large amount of hidden information present in a dataset, which cannot be explored by the traditional machine learning (ML) algorithms. There is a scarcity of studies that reveal the use of deep neural networks (DNNs) in the domain of incident prediction, and its parameter optimization for achieving better prediction power. To address these issues, initially, key terms are extracted from the unstructured texts using LDA-based topic modeling. Then, these key terms are added with the predictor categories to form the feature vector, which is further processed for noise reduction and fed to the adaptive moment estimation (ADAM)-based DNN (i.e., ADNN) for classification, as ADAM is superior to GD, SGD, and RMSProp. To evaluate the effectiveness of our proposed method, a comparative study has been conducted using some state-of-the-arts on five benchmark datasets. Moreover, a case study of an integrated steel plant in India has been demonstrated for the validation of the proposed model. Experimental results reveal that ADNN produces superior performance than others in terms of accuracy. Therefore, the present study offers a robust methodological guide that enables us to handle the issues of unstructured data and hidden information for developing a predictive model.
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A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study. Comput Biol Med 2022; 140:105102. [PMID: 34973521 DOI: 10.1016/j.compbiomed.2021.105102] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 11/29/2021] [Accepted: 11/29/2021] [Indexed: 12/17/2022]
Abstract
MOTIVATION Machine learning (ML) algorithms can provide better cardiovascular event (CVE) prediction. However, ML algorithms are mostly explored for predicting a single CVE at a time. The objective of this study is to design and develop an ML-based system to predict multi-label CVEs, such as (i) coronary artery disease, (ii) acute coronary syndrome, and (iii) a composite CVE-a class of AtheroEdge 3.0 (ML) system. METHODS Focused carotid B-mode ultrasound and coronary angiography are performed on a group of 459 participants consisting of three cardiovascular labels. Initially, 23 risk predictors comprising (i) patients' demographics, (ii) clinical blood-biomarkers, and (iii) carotid ultrasound image-based phenotypes are collected. Six types of classification techniques comprising (a) four problem transformation methods (PTM) and (b) two algorithm adaptation methods (AAM) are used for multi-label CVE prediction. The performance of the proposed system is evaluated for accuracy, sensitivity, specificity, F1-score, and area-under-the-curve (AUC) using 10-fold cross-validation. The proposed system is also verified using another database of 522 participants. RESULTS For the primary database, PTM demonstrated a better multi-label CVE prediction than AAM (mean accuracy: 80.89% vs. 62.83%, mean AUC: 0.89 vs. 0.63), validating our hypothesis. The PTM-based binary relevance (BR) technique provided optimal performance in multi-label CVE prediction. The overall multi-label classification accuracy, sensitivity, specificity, F1-score, and AUC using BR are 81.2 ± 3.01%, 76.5 ± 8.8%, 83.8 ± 3.8%, 75.37 ± 5.8%, and 0.89 ± 0.02 (p < 0.0001), respectively. When used on the second Canadian database with seven cardiovascular events (acute coronary syndrome, myocardial infarction, angina, stroke, transient ischemic attack, heart failure, and death), the proposed system showed an accuracy of 96.36 ± 0.87% (AUC: 0.61 ± 0.06, p < 0.0001). CONCLUSION ML-based multi-label classification algorithms, such as binary relevance, yielded the best predictions for three cardiovascular endpoints.
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45
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Gupta N, Gupta SK, Pathak RK, Jain V, Rashidi P, Suri JS. Human activity recognition in artificial intelligence framework: a narrative review. Artif Intell Rev 2022; 55:4755-4808. [PMID: 35068651 PMCID: PMC8763438 DOI: 10.1007/s10462-021-10116-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10116-x.
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Affiliation(s)
- Neha Gupta
- CSE Department, Bennett University, Greater Noida, UP India
- Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, India
| | | | | | - Vanita Jain
- Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, New Delhi, India
| | - Parisa Rashidi
- Intelligent Health Laboratory, Department of Biomedical Engineering, University of Florida, Gainesville, USA
| | - Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPointTM, Roseville, CA 95661 USA
- Global Biomedical Technologies, Inc., Roseville, CA USA
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Zemmal N, Benzebouchi NE, Azizi N, Schwab D, Belhaouari SB. Unbalanced Learning for Diabetes Diagnosis Based on Enhanced Resampling and Stacking Classifier. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2022. [DOI: 10.4018/ijiit.309583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Diabetes is characterized by an abnormally enhanced concentration of glucose in the blood serum. It has a damaging impact on several noble body systems. Today, the concept of unbalanced learning has developed considerably in the domain of medical diagnosis, which greatly reduces the generation of erroneous classification results. The paper takes a hybrid approach to imbalanced learning by proposing an enhanced multimodal meta-learning method called IRESAMPLE+St to distinguish between normal and diabetic patients. This approach relies on the Stacking paradigm by utilizing the complementarity that may exist between classifiers. In the same focus of this study, a modified RESAMPLE-based technique referred to as IRESAMPLE+ and the SMOTE method are integrated as a preliminary resampling step to overcome and resolve the problem of unbalanced data. The suggested IRESAMPLE+St provides a computerized diabetes diagnostic system with impressive results, comparing it to the principal related studies, reflecting the design and engineering successes achieved.
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Affiliation(s)
- Nawel Zemmal
- Mathematics and Computer Science Department, Mohamed Cherif Messaadia University, Souk-Ahras, Algeria & Labged Laboratory, Badji Mokhtar Annaba University, Annaba, Algeria
| | - Nacer Eddine Benzebouchi
- Labged Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria
| | - Nabiha Azizi
- Labged Laboratory, Computer Science Department, Badji Mokhtar Annaba University, Annaba, Algeria
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Fregoso-Aparicio L, Noguez J, Montesinos L, García-García JA. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab Syndr 2021; 13:148. [PMID: 34930452 PMCID: PMC8686642 DOI: 10.1186/s13098-021-00767-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022] Open
Abstract
Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model's efficiency. Models trained on tidy datasets achieved almost perfect models.
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Affiliation(s)
- Luis Fregoso-Aparicio
- School of Engineering and Sciences, Tecnologico de Monterrey, Av Lago de Guadalupe KM 3.5, Margarita Maza de Juarez, 52926 Cd Lopez Mateos, Mexico
| | - Julieta Noguez
- School of Engineering and Sciences, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849 Monterrey, Nuevo Leon Mexico
| | - Luis Montesinos
- School of Engineering and Sciences, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849 Monterrey, Nuevo Leon Mexico
| | - José A. García-García
- Hospital General de Mexico Dr. Eduardo Liceaga, Dr. Balmis 148, Doctores, Cuauhtemoc, 06720 Mexico City, Mexico
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A Fuzzy Rule-Based System for Classification of Diabetes. SENSORS 2021; 21:s21238095. [PMID: 34884099 PMCID: PMC8659829 DOI: 10.3390/s21238095] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/27/2021] [Accepted: 11/28/2021] [Indexed: 12/26/2022]
Abstract
Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.
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Sanagala SS, Nicolaides A, Gupta SK, Koppula VK, Saba L, Agarwal S, Johri AM, Kalra MS, Suri JS. Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification. Diagnostics (Basel) 2021; 11:2109. [PMID: 34829456 PMCID: PMC8622690 DOI: 10.3390/diagnostics11112109] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/03/2021] [Accepted: 11/09/2021] [Indexed: 12/24/2022] Open
Abstract
Background and Purpose: Only 1-2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches-a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i-ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv-v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.
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Affiliation(s)
- Skandha S. Sanagala
- CSE Department, CMR College of Engineering & Technology, Hyderabad 501401, TS, India; (S.S.S.); (V.K.K.)
- CSE Department, Bennett University, Greater Noida 203206, UP, India;
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia 1700, Cyprus;
| | - Suneet K. Gupta
- CSE Department, Bennett University, Greater Noida 203206, UP, India;
| | - Vijaya K. Koppula
- CSE Department, CMR College of Engineering & Technology, Hyderabad 501401, TS, India; (S.S.S.); (V.K.K.)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | | | - Amer M. Johri
- Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Manudeep S. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA;
| | - Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA 95661, USA
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50
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Jeyafzam F, Vaziri B, Suraki MY, Hosseinabadi AAR, Slowik A. Improvement of grey wolf optimizer with adaptive middle filter to adjust support vector machine parameters to predict diabetes complications. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-06143-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractIn medical science, collecting and classifying data from various diseases is a vital task. The confused and large amounts of data are problems that prevent us from achieving acceptable results. One of the major problems for diabetic patients is a failure to properly diagnose the disease. As a result of this mistake in diagnosis or failure in early diagnosis, the patient may suffer from complications such as blindness, kidney failure, and cutting off the toes. Nowadays, doctors diagnose the disease by relying on their experience and knowledge and performing complex and time-consuming tests. One of the problems with current diabetic, diagnostic methods is the lack of appropriate features to diagnose the disease and consequently the weakness in its diagnosis, especially in its early stages. Since diabetes diagnosis relies on large amounts of data with many parameters, it is necessary to use machine learning methods such as support vector machine (SVM) to predict the complications of diabetes. One of the disadvantages of SVM is its parameter adjustment, which can be accomplished using metaheuristic algorithms such as particle swarm optimization algorithm (PSO), genetic algorithm, or grey wolf optimizer (GWO). In this paper, after preprocessing and preparing the dataset for data mining, we use SVM to predict complications of diabetes based on selected parameters of a patient acquired by laboratory test using improved GWO. We improve the selection process of GWO by employing dynamic adaptive middle filter, a nonlinear filter that assigns appropriate weight to each value based on the data value. Comparison of the final results of the proposed algorithm with classification methods such as a multilayer perceptron neural network, decision tree, simple Bayes, and temporal fuzzy min–max neural network (TFMM-PSO) shows the superiority of the proposed method over the comparable ones.
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