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Fathima AJ, Fasla MMN. A comprehensive review on heart disease prognostication using different artificial intelligence algorithms. Comput Methods Biomech Biomed Engin 2024; 27:1357-1374. [PMID: 38424704 DOI: 10.1080/10255842.2024.2319706] [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: 01/30/2023] [Revised: 02/06/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Prediction of heart diseases on time is significant in order to preserve life. Many conventional methods have taken efforts on earlier prediction but faced with challenges of higher prediction cost, extended time for computation and complexities with larger volume of data which reduced prediction accuracy. In order to overcome such pitfalls, AI (Artificial Intelligence) technology has been evolved in diagnosing heart diseases through deployment of several ML (Machine Learning) and DL (Deep Learning) algorithms. It improves detection by influencing with its capacity of learning from the massive data containing age, obesity, hypertension and other risk factors of patients and extract it accordingly to differentiate on the circumstances. Moreover, storage of larger data with AI greatly assists in analysing the occurrence of the disease from past historical data. Hence, this paper intends to provide a review on different AI based algorithms used in the heart disease prognostication and delivers its benefits through researching on various existing works. It performs comparative analysis and critical assessment as encompassing accuracies and maximum utilization of algorithms focussed by traditional studies in this area. The major findings of the paper emphasized on the evolution and continuous explorations of AI techniques for heart disease prediction and the future researchers aims in determining the dimensions that have attained high and low prediction accuracies on which appropriate research works can be performed. Finally, future research is included to offer new stimulus for further investigation of AI in cardiac disease diagnosis.
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Affiliation(s)
- A Jainul Fathima
- Assistant Professor, IT Francis Xavier Engineering College, Tirunelveli - 627003, India
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Yang KF, Li SJ, Xu J, Zheng YB. Machine learning prediction model for gray-level co-occurrence matrix features of synchronous liver metastasis in colorectal cancer. World J Gastrointest Surg 2024; 16:1571-1581. [PMID: 38983351 PMCID: PMC11229995 DOI: 10.4240/wjgs.v16.i6.1571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/16/2024] [Accepted: 04/25/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Synchronous liver metastasis (SLM) is a significant contributor to morbidity in colorectal cancer (CRC). There are no effective predictive device integration algorithms to predict adverse SLM events during the diagnosis of CRC. AIM To explore the risk factors for SLM in CRC and construct a visual prediction model based on gray-level co-occurrence matrix (GLCM) features collected from magnetic resonance imaging (MRI). METHODS Our study retrospectively enrolled 392 patients with CRC from Yichang Central People's Hospital from January 2015 to May 2023. Patients were randomly divided into a training and validation group (3:7). The clinical parameters and GLCM features extracted from MRI were included as candidate variables. The prediction model was constructed using a generalized linear regression model, random forest model (RFM), and artificial neural network model. Receiver operating characteristic curves and decision curves were used to evaluate the prediction model. RESULTS Among the 392 patients, 48 had SLM (12.24%). We obtained fourteen GLCM imaging data for variable screening of SLM prediction models. Inverse difference, mean sum, sum entropy, sum variance, sum of squares, energy, and difference variance were listed as candidate variables, and the prediction efficiency (area under the curve) of the subsequent RFM in the training set and internal validation set was 0.917 [95% confidence interval (95%CI): 0.866-0.968] and 0.09 (95%CI: 0.858-0.960), respectively. CONCLUSION A predictive model combining GLCM image features with machine learning can predict SLM in CRC. This model can assist clinicians in making timely and personalized clinical decisions.
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Affiliation(s)
- Kai-Feng Yang
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
| | - Sheng-Jie Li
- Department of Gastrointestinal Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People’s Hospital, Yichang 443008, Hubei Province, China
| | - Jun Xu
- Department of Gastrointestinal Surgery, The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People’s Hospital, Yichang 443008, Hubei Province, China
| | - Yong-Bin Zheng
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan 430030, Hubei Province, China
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Sufian MA, Hamzi W, Zaman S, Alsadder L, Hamzi B, Varadarajan J, Azad MAK. Enhancing Clinical Validation for Early Cardiovascular Disease Prediction through Simulation, AI, and Web Technology. Diagnostics (Basel) 2024; 14:1308. [PMID: 38928723 PMCID: PMC11202579 DOI: 10.3390/diagnostics14121308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/10/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Cardiovascular diseases (CVDs) remain a major global health challenge and a leading cause of mortality, highlighting the need for improved predictive models. We introduce an innovative agent-based dynamic simulation technique that enhances our AI models' capacity to predict CVD progression. This method simulates individual patient responses to various cardiovascular risk factors, improving prediction accuracy and detail. Also, by incorporating an ensemble learning model and interface of web application in the context of CVD prediction, we developed an AI dashboard-based model to enhance the accuracy of disease prediction and provide a user-friendly app. The performance of traditional algorithms was notable, with Ensemble learning and XGBoost achieving accuracies of 91% and 95%, respectively. A significant aspect of our research was the integration of these models into a streamlit-based interface, enhancing user accessibility and experience. The streamlit application achieved a predictive accuracy of 97%, demonstrating the efficacy of combining advanced AI techniques with user-centered web applications in medical prediction scenarios. This 97% confidence level was evaluated by Brier score and calibration curve. The design of the streamlit application facilitates seamless interaction between complex ML models and end-users, including clinicians and patients, supporting its use in real-time clinical settings. While the study offers new insights into AI-driven CVD prediction, we acknowledge limitations such as the dataset size. In our research, we have successfully validated our predictive proposed methodology against an external clinical setting, demonstrating its robustness and accuracy in a real-world fixture. The validation process confirmed the model's efficacy in the early detection of CVDs, reinforcing its potential for integration into clinical workflows to aid in proactive patient care and management. Future research directions include expanding the dataset, exploring additional algorithms, and conducting clinical trials to validate our findings. This research provides a valuable foundation for future studies, aiming to make significant strides against CVDs.
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Affiliation(s)
- Md Abu Sufian
- IVR Low-Carbon Research Institute, Chang’an University, Xi’an 710018, China;
- School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Wahiba Hamzi
- Laboratoire de Biotechnologie Santé et Environnement, Department of Biology, University of Blida, Blida 09000, Algeria;
| | - Sadia Zaman
- Department of Physiology, Queen Mary University, London E1 4NS, UK; (S.Z.); (L.A.)
| | - Lujain Alsadder
- Department of Physiology, Queen Mary University, London E1 4NS, UK; (S.Z.); (L.A.)
| | - Boumediene Hamzi
- Department of Computing and Mathematical Sciences, California Institute of Technology, Caltech, CA 91125, USA;
- The Alan Turing Institute, London NW1 2DB, UK
- Department of Mathematics, Gulf University for Science and Technology (GUST), Mubarak Al-Abdullah 32093, Kuwait
| | - Jayasree Varadarajan
- Centre for Digital Innovation, Manchester Metropolitan University, Manchester M15 6BH, UK;
| | - Md Abul Kalam Azad
- Department of Medicine, Rangpur Medical College and Hospital, Rangpur 5400, Bangladesh
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Binson VA, Thomas S, Subramoniam M, Arun J, Naveen S, Madhu S. A Review of Machine Learning Algorithms for Biomedical Applications. Ann Biomed Eng 2024; 52:1159-1183. [PMID: 38383870 DOI: 10.1007/s10439-024-03459-3] [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: 12/30/2023] [Accepted: 01/24/2024] [Indexed: 02/23/2024]
Abstract
As the amount and complexity of biomedical data continue to increase, machine learning methods are becoming a popular tool in creating prediction models for the underlying biomedical processes. Although all machine learning methods aim to fit models to data, the methodologies used can vary greatly and may seem daunting at first. A comprehensive review of various machine learning algorithms per biomedical applications is presented. The key concepts of machine learning are supervised and unsupervised learning, feature selection, and evaluation metrics. Technical insights on the major machine learning methods such as decision trees, random forests, support vector machines, and k-nearest neighbors are analyzed. Next, the dimensionality reduction methods like principal component analysis and t-distributed stochastic neighbor embedding methods, and their applications in biomedical data analysis were reviewed. Moreover, in biomedical applications predominantly feedforward neural networks, convolutional neural networks, and recurrent neural networks are utilized. In addition, the identification of emerging directions in machine learning methodology will serve as a useful reference for individuals involved in biomedical research, clinical practice, and related professions who are interested in understanding and applying machine learning algorithms in their research or practice.
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Affiliation(s)
- V A Binson
- Department of Electronics Engineering, Saintgits College of Engineering, Kottayam, India
| | - Sania Thomas
- Department of Computer Science and Engineering, Saintgits College of Engineering, Kottayam, India
| | - M Subramoniam
- Department of Electronics Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
| | - J Arun
- Centre for Waste Management-International Research Centre, Sathyabama Institute of Science and Technology, Chennai, 600119, India
| | - S Naveen
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
| | - S Madhu
- Department of Automobile Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India.
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Wei J, Pan B, Gan Y, Li X, Liu D, Sang B, Gao X. Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis. SENSORS (BASEL, SWITZERLAND) 2024; 24:2705. [PMID: 38732812 PMCID: PMC11085865 DOI: 10.3390/s24092705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/07/2024] [Accepted: 03/11/2024] [Indexed: 05/13/2024]
Abstract
The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary artery disease (CAD). Despite its widespread use, TET reports are susceptible to external influences, heightening the risk of misdiagnosis and underdiagnosis. In this paper, we propose a novel automatic CAD diagnosis approach. The proposed approach introduces a customized preprocessing method to obtain clear electrocardiograms (ECGs) from individual TET reports. Additionally, it presents TETDiaNet, a novel neural network designed to explore the temporal relationships within TET ECGs. Central to TETDiaNet is the TETDia block, which mimics clinicians' diagnostic processes to extract essential diagnostic information. This block encompasses an intra-state contextual learning module and an inter-state contextual learning module, modeling the temporal relationships within a single state and between states, respectively. These two modules help the TETDia block to capture effective diagnosis information by exploring the temporal relationships within TET ECGs. Furthermore, we establish a new TET dataset named TET4CAD for CAD diagnosis. It contains simplified TET reports for 192 CAD patients and 224 non-CAD patients, and each patient undergoes coronary angiography for labeling. Experimental results on TET4CAD underscore the superior performance of the proposed approach, highlighting the discriminative value of the temporal relationships within TET ECGs for CAD diagnosis.
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Affiliation(s)
- Jianze Wei
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (J.W.); (B.P.)
| | - Bocheng Pan
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (J.W.); (B.P.)
| | - Yu Gan
- Cardiology Department, Beijing Hospital, Beijing 100730, China; (Y.G.); (X.L.); (B.S.)
- National Center of Gerontology, National Health Commission Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Xuedi Li
- Cardiology Department, Beijing Hospital, Beijing 100730, China; (Y.G.); (X.L.); (B.S.)
- National Center of Gerontology, National Health Commission Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Deping Liu
- Cardiology Department, Beijing Hospital, Beijing 100730, China; (Y.G.); (X.L.); (B.S.)
- National Center of Gerontology, National Health Commission Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Botao Sang
- Cardiology Department, Beijing Hospital, Beijing 100730, China; (Y.G.); (X.L.); (B.S.)
- University of Chinese Academy of Sciences, Beijing 100006, China
| | - Xingyu Gao
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China; (J.W.); (B.P.)
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Terzi MB, Arikan O. Machine learning based hybrid anomaly detection technique for automatic diagnosis of cardiovascular diseases using cardiac sympathetic nerve activity and electrocardiogram. BIOMED ENG-BIOMED TE 2024; 69:79-109. [PMID: 37823386 DOI: 10.1515/bmt-2022-0406] [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: 10/19/2022] [Accepted: 08/25/2023] [Indexed: 10/13/2023]
Abstract
OBJECTIVES Coronary artery diseases (CADs) are the leading cause of death worldwide and early diagnosis is crucial for timely treatment. To address this, our study presents a novel automated Artificial Intelligence (AI)-based Hybrid Anomaly Detection (AIHAD) technique that combines various signal processing, feature extraction, supervised, and unsupervised machine learning methods. By jointly and simultaneously analyzing 12-lead cardiac sympathetic nerve activity (CSNA) and electrocardiogram (ECG) data, the automated AIHAD technique performs fast, early, and accurate diagnosis of CADs. METHODS In order to develop and evaluate the proposed automated AIHAD technique, we utilized the fully labeled STAFF III and PTBD databases, which contain the 12-lead wideband raw recordings non-invasively acquired from 260 subjects. Using these wideband raw recordings, we developed a signal processing technique that simultaneously detects the 12-lead CSNA and ECG signals of all subjects. Using the pre-processed 12-lead CSNA and ECG signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of CADs. Using the extracted discriminative features, we developed a supervised classification technique based on Artificial Neural Networks (ANNs) that simultaneously detects anomalies in the 12-lead CSNA and ECG data. Furthermore, we developed an unsupervised clustering technique based on Gaussian mixture models (GMMs) and Neyman-Pearson criterion, which robustly detects outliers corresponding to CADs. RESULTS Using the automated AIHAD technique, we have, for the first time, demonstrated a significant association between the increase in CSNA signals and anomalies in ECG signals during CADs. The AIHAD technique achieved highly reliable detection of CADs with a sensitivity of 98.48 %, specificity of 97.73 %, accuracy of 98.11 %, positive predictive value of 97.74 %, negative predictive value of 98.47 %, and F1-score of 98.11 %. Hence, the automated AIHAD technique demonstrates superior performance compared to the gold standard diagnostic test ECG in the diagnosis of CADs. Additionally, it outperforms other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it significantly increases the detection performance of CADs by taking advantage of the diversity in different data types and leveraging their strengths. Furthermore, its performance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or classify CADs. Additionally, it has a very low implementation time, which is highly desirable for real-time detection of CADs. CONCLUSIONS The proposed automated AIHAD technique may serve as an efficient decision-support system to increase physicians' success in fast, early, and accurate diagnosis of CADs. It may be highly beneficial and valuable, particularly for asymptomatic patients, for whom the diagnostic information provided by ECG alone is not sufficient to reliably diagnose the disease. Hence, it may significantly improve patient outcomes by enabling timely treatments and considerably reducing the mortality of cardiovascular diseases (CVDs).
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Affiliation(s)
- Merve Begum Terzi
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
| | - Orhan Arikan
- Faculty of Engineering, Electrical and Electronics Engineering Department, Bilkent University, Ankara, Türkiye
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Han Y, Zhao Y, Lin Z, Liang Z, Chen S, Zhang J. Differential diagnosis between dilated cardiomyopathy and ischemic cardiomyopathy based on variational mode decomposition and high order spectra analysis. Health Inf Sci Syst 2023; 11:43. [PMID: 37744026 PMCID: PMC10511396 DOI: 10.1007/s13755-023-00244-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 08/26/2023] [Indexed: 09/26/2023] Open
Abstract
The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM). The definitive diagnosis of DCM necessitates the identification of invasive, costly, and contraindicated coronary angiography. Many diagnostic studies of cardiovascular disease have tried modal decomposition based on electrocardiogram (ECG) signals. However, these studies ignored the connection between modes and other fields, thus limiting the interpretability of modes to ECG signals and the classification performance of models. This study proposes a classification algorithm based on variational mode decomposition (VMD) and high order spectra, which decomposes the preprocessed ECG signal and extracts its first five modes obtained through VMD. After that, these modes are estimated for their corresponding bispectrums, and the feature vector is composed of fifteen features including bispectral, frequency, and nonlinear features based on this. Finally, a dataset containing 75 subjects (38 DCM, 37 ICM) is classified and compared using random forest (RF), decision tree, support vector machine, and K-nearest neighbor. The results show that, in comparison to previous approaches, the technique proposed provides a better categorization for DCM and ICM of ECG signals, which delivers 98.21% classification accuracy, 98.22% sensitivity, and 98.19% specificity. And mode 3 always has the best performance among single mode. The proposed computerized framework significantly improves automatic diagnostic performance, which can help relieve the working pressure on doctors, possible economic burden and health threaten.
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Affiliation(s)
- Yuduan Han
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Yunyue Zhao
- Department of Cardiology, The Third Affiliated Hospital of Sun Yat‐sen University, Guangzhou, China
| | - Zhuochen Lin
- Department of Medical Records, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zichao Liang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Siyang Chen
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Jinxin Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China
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Li L, Cui X, Yang J, Wu X, Zhao G. Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes after in vitro fertilization. Front Endocrinol (Lausanne) 2023; 14:1305473. [PMID: 38093967 PMCID: PMC10716466 DOI: 10.3389/fendo.2023.1305473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 11/09/2023] [Indexed: 12/18/2023] Open
Abstract
Background According to a recent report by the WHO, approximately 17.5\% (about one-sixth) of the global adult population is affected by infertility. Consequently, researchers worldwide have proposed various machine learning models to improve the prediction of clinical pregnancy outcomes during IVF cycles. The objective of this study is to develop a machine learning(ML) model that predicts the outcomes of pregnancies following in vitro fertilization (IVF) and assists in clinical treatment. Methods This study conducted a retrospective analysis on provincial reproductive centers in China from March 2020 to March 2021, utilizing 13 selected features. The algorithms used included XGBoost, LightGBM, KNN, Naïve Bayes, Random Forest, and Decision Tree. The results were evaluated using performance metrics such as precision, recall, F1-score, accuracy and AUC, employing five-fold cross-validation repeated five times. Results Among the models, LightGBM achieved the best performance, with an accuracy of 92.31%, recall of 87.80%, F1-score of 90.00\%, and an AUC of 90.41%. The model identified the estrogen concentration at the HCG injection(etwo), endometrium thickness (mm) on HCG day(EM TNK), years of infertility(Years), and body mass index(BMI) as the most important features. Conclusion This study successfully demonstrates the LightGBM model has the best predictive effect on pregnancy outcomes during IVF cycles. Additionally, etwo was found to be the most significant predictor for successful IVF compared to other variables. This machine learning approach has the potential to assist fertility specialists in providing counseling and adjusting treatment strategies for patients.
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Affiliation(s)
- Lu Li
- School of Basic Medicine, Anhui Medical University, Hefei, China
- Center of Reproductive Medicine, Children’s Hospital of Shanxi and Women Health Center of Shanxi, Taiyuan, China
| | - Xiangrong Cui
- Center of Reproductive Medicine, Children’s Hospital of Shanxi and Women Health Center of Shanxi, Taiyuan, China
| | - Jian Yang
- School of Information, Shanxi University of Finance and Economics, Taiyuan, China
| | - Xueqing Wu
- Center of Reproductive Medicine, Children’s Hospital of Shanxi and Women Health Center of Shanxi, Taiyuan, China
| | - Gang Zhao
- School of Basic Medicine, Anhui Medical University, Hefei, China
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China
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Prasad SS, Deo RC, Salcedo-Sanz S, Downs NJ, Casillas-Pérez D, Parisi AV. Enhanced joint hybrid deep neural network explainable artificial intelligence model for 1-hr ahead solar ultraviolet index prediction. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107737. [PMID: 37573641 DOI: 10.1016/j.cmpb.2023.107737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/16/2023] [Accepted: 07/27/2023] [Indexed: 08/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Exposure to solar ultraviolet (UV) radiation can cause malignant keratinocyte cancer and eye disease. Developing a user-friendly, portable, real-time solar UV alert system especially or wearable electronic mobile devices can help reduce the exposure to UV as a key measure for personal and occupational management of the UV risks. This research aims to design artificial intelligence-inspired early warning tool tailored for short-term forecasting of UV index (UVI) integrating satellite-derived and ground-based predictors for Australian hotspots receiving high UV exposures. The study further improves the trustworthiness of the newly designed tool using an explainable artificial intelligence approach. METHODS An enhanced joint hybrid explainable deep neural network model (called EJH-X-DNN) is constructed involving two phases of feature selection and hyperparameter tuning using Bayesian optimization. A comprehensive assessment of EJH-X- DNN is conducted with six other competing benchmarked models. The proposed model is explained locally and globally using robust model-agnostic explainable artificial intelligence frameworks such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley additive explanations (SHAP), and permutation feature importance (PFI). RESULTS The newly proposed model outperformed all benchmarked models for forecasting hourly horizons UVI, with correlation coefficients of 0.900, 0.960, 0.897, and 0.913, respectively, for Darwin, Alice Springs, Townsville, and Emerald hotspots. According to the combined local and global explainable model outcomes, the site-based results indicate that antecedent lagged memory of UVI and solar zenith angle are influential features. Predictions made by EJH-X-DNN model are strongly influenced by factors such as ozone effect, cloud conditions, and precipitation. CONCLUSION With its superiority and skillful interpretation, the UVI prediction system reaffirms its benefits for providing real-time UV alerts to mitigate risks of skin and eye health complications, reducing healthcare costs and contributing to outdoor exposure policy.
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Affiliation(s)
- Salvin S Prasad
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
| | - Ravinesh C Deo
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia.
| | - Sancho Salcedo-Sanz
- School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia; Department of Signal Processing and Communications, Universidad de Alcalá, Alcalá de Henares, 28805, Madrid, Spain.
| | - Nathan J Downs
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
| | - David Casillas-Pérez
- Department of Signal Processing and Communications, Universidad Rey Juan Carlos, Fuenlabrada, 28942, Madrid, Spain.
| | - Alfio V Parisi
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia.
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Liu Y, Huang W, Yang J, Yuan S, Li C, Wang W, Liang Z, Wu A. Construction of a multi-classified decision tree model for identifying malignant pleural effusion and tuberculous pleural effusion. Clin Biochem 2023; 120:110655. [PMID: 37769933 DOI: 10.1016/j.clinbiochem.2023.110655] [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: 07/02/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/03/2023]
Abstract
OBJECTIVE Pleural effusion (PE) is a common clinical complication associated with various disorders. We aimed to utilize laboratory variables and their corresponding ratios in serum and PE for the differential diagnosis of multiple types of PE based on a decision tree (DT) algorithm. METHODS A total of 1435 untreated patients with PE admitted to The First Affiliated Hospital of Ningbo University were enrolled. The demographic and laboratory variables were collected and compared. The receiver operating characteristic curve was used to select important variables for diagnosing malignant pleural effusion (MPE) or tuberculous pleural effusion (TPE) and included in the DT model. The data were divided into the training set and the test set at a ratio of 7:3. The training data was used to develop the DT model, and the test data was for evaluating the model. Independent data was collected as external validation. RESULTS Three PE indicators (carcinoembryonic antigen, adenosine deaminase [ADA], and total protein), two serum indicators (neuron-specific enolase and cytokeratin 19 fragments), and two ratios [high-sensitivity C-reactive protein (hsCRP)/ PE lymphocyte and hsCRP/PE ADA] were used to construct the DT model. The area under the curve (AUC), sensitivity, and specificity for diagnosing MPE were 0.963, 84.0%, 91.6% in the training set, 0.976, 84.1%, 88.6% in the test set, and 0.955,83.3%, 86.7% in the external validation set. The AUC, sensitivity, and specificity of diagnosing TPE were 0.898, 86.8%, 92.3% in the training set, 0.888, 88.8%, 92.7% in the test set, and 0.778, 84.8%, 94.3% in the external validation set. CONCLUSION The DT model showed good diagnostic efficacy and could be applied for the differential diagnosis of MPE and TPE in clinical settings.
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Affiliation(s)
- Yanqing Liu
- Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Weina Huang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Jing Yang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Songbo Yuan
- Department of Laboratory Medicine, the Affiliated People's Hospital of Ningbo University, Ningbo, Zhejiang, China
| | - Congcong Li
- Hangzhou DIAN Medical Diagnostics Laboratory, Hangzhou, Zhejiang, China
| | - Weiwei Wang
- School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Zhigang Liang
- Department of Thoracic Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
| | - Aihua Wu
- Department of Laboratory Medicine, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
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Carioti D, Stucchi NA, Toneatto C, Masia MF, Del Monte M, Stefanelli S, Travellini S, Marcelli A, Tettamanti M, Vernice M, Guasti MT, Berlingeri M. The ReadFree tool for the identification of poor readers: a validation study based on a machine learning approach in monolingual and minority-language children. ANNALS OF DYSLEXIA 2023; 73:356-392. [PMID: 37548832 PMCID: PMC10522748 DOI: 10.1007/s11881-023-00287-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
In this study, we validated the "ReadFree tool", a computerised battery of 12 visual and auditory tasks developed to identify poor readers also in minority-language children (MLC). We tested the task-specific discriminant power on 142 Italian-monolingual participants (8-13 years old) divided into monolingual poor readers (N = 37) and good readers (N = 105) according to standardised Italian reading tests. The performances at the discriminant tasks of the "ReadFree tool" were entered into a classification and regression tree (CART) model to identify monolingual poor and good readers. The set of classification rules extracted from the CART model were applied to the MLC's performance and the ensuing classification was compared to the one based on standardised Italian reading tests. According to the CART model, auditory go-no/go (regular), RAN and Entrainment100bpm were the most discriminant tasks. When compared with the clinical classification, the CART model accuracy was 86% for the monolinguals and 76% for the MLC. Executive functions and timing skills turned out to have a relevant role in reading. Results of the CART model on MLC support the idea that ad hoc standardised tasks that go beyond reading are needed.
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Affiliation(s)
- Desiré Carioti
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
- Psychology Department, University of Milano-Bicocca, Milan, Italy
| | | | - Carlo Toneatto
- Psychology Department, University of Milano-Bicocca, Milan, Italy
| | - Marta Franca Masia
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
| | - Milena Del Monte
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
- Center of Developmental Neuropsychology, AST Pesaro-Urbino, Distretto di Pesaro, Pesaro, Italy
| | - Silvia Stefanelli
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
- Department of Human Sciences, University of the Republic of San Marino, San Marino, Republic of San Marino
| | - Simona Travellini
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
- Center of Developmental Neuropsychology, AST Pesaro-Urbino, Distretto di Pesaro, Pesaro, Italy
| | - Antonella Marcelli
- Center of Developmental Neuropsychology, AST Pesaro-Urbino, Distretto di Pesaro, Pesaro, Italy
| | - Marco Tettamanti
- Psychology Department, University of Milano-Bicocca, Milan, Italy
| | - Mirta Vernice
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
| | | | - Manuela Berlingeri
- DISTUM, Department of Humanities, University of Urbino Carlo Bo, Urbino, Italy
- Center of Developmental Neuropsychology, AST Pesaro-Urbino, Distretto di Pesaro, Pesaro, Italy
- NeuroMi, Milan Center for Neuroscience, Milan, Italy
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Shen K, Kejriwal M. Quantifying COVID-19 policy impacts on subjective well-being during the early phase of the pandemic: A cross-sectional analysis of United States survey data from March to August 2020. PLoS One 2023; 18:e0291494. [PMID: 37733714 PMCID: PMC10513291 DOI: 10.1371/journal.pone.0291494] [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: 01/23/2023] [Accepted: 08/19/2023] [Indexed: 09/23/2023] Open
Abstract
To stop the spread of COVID-19, a number of public health policies and restrictions were implemented during the pre-vaccination phase of the pandemic. This study provides a quantitative assessment of how these policies impacted subjective well-being (SWB) in the United States over a 6-month period spanning March to August 2020. We study two specific research objectives. First, we aim to quantify the impacts of COVID-19 public health policies at different levels of stringency on SWB. Second, we train and implement a conditional inference tree model for predicting individual SWB based both on socio-demographic characteristics and policies then in place. Our results indicate that policies such as enforcing strict stay-at-home requirements and closing workplaces were negatively associated with SWB, and that an individual's socio-demographic characteristics, including income status, job, and gender, conditionally interact with policies such as workplace closure in a predictive model of SWB. Therefore, although such policies may have positive health implications, they also have secondary environmental and social implications that need to be taken into account in any cost-benefit analysis of such policies for future pandemic preparedness. Our proposed methodology suggests a way to quantify such impacts through the lens of SWB, and to further advance the science of pandemic preparedness from a public health perspective.
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Affiliation(s)
- Ke Shen
- Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California, United States of America
| | - Mayank Kejriwal
- Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California, United States of America
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Bittencourt RA, Pereira HBDB, Moret MA, Galam S, Lima ICDC. Interplay of self, epiphany, and positive actions in shaping individual careers. Phys Rev E 2023; 108:024314. [PMID: 37723784 DOI: 10.1103/physreve.108.024314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 07/10/2023] [Indexed: 09/20/2023]
Abstract
In this work, we model an individual social career by a finite-size trajectory along a hexagonal lattice moving only forward. At each bifurcation, the individual makes a free-will choice to follow one or the other branch within an uncertain outcome. Considering that those choices are determined by an individual self built from endogenous characteristics, we assume they are random following a binomial distribution. As a result, the individual ascends or descends on the social scale via random progress through the series of bifurcations made at the encountered junctions. The related stochastic process is found to be diffusive. For different selves coming from different points on the social scale, progress does overlap. In addition, we include the possibility of continuous transition across the lattice caused external influences as an epiphany. The occurrence of a quantum leap resulting from an affirmative action opportunity is also included. We also treat the case of a social group being acted by a collective epiphany as with education. The results highlight the key effect of epiphanies and quantum leaps to promote upward mobility across social classes.
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Affiliation(s)
- Rafael A Bittencourt
- Centro Universitário SENAI-Cimatec, Salvador, 41650-010 Bahia, Brazil
- Instituto Nacional de Ciência e Tecnologia-Geofísica do Petróleo, Salvador, 40170-290 Bahia, Brazil
| | - H B de B Pereira
- Centro Universitário SENAI-Cimatec, Salvador, 41650-010 Bahia, Brazil
- Bahia State University, Salvador, 41180-045 Bahia, Brazil
| | - M A Moret
- Centro Universitário SENAI-Cimatec, Salvador, 41650-010 Bahia, Brazil
- Bahia State University, Salvador, 41180-045 Bahia, Brazil
| | - S Galam
- Centre for Political Research, 75007 Paris, France
- Centre National de la Recherche Scientifique, 75016 Paris, France
| | - I C da Cunha Lima
- Centro Universitário SENAI-Cimatec, Salvador, 41650-010 Bahia, Brazil
- Instituto Nacional de Ciência e Tecnologia-Geofísica do Petróleo, Salvador, 40170-290 Bahia, Brazil
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14
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Redekar SS, Varma SL, Bhattacharjee A. Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings. J Egypt Natl Canc Inst 2023; 35:22. [PMID: 37482563 DOI: 10.1186/s43046-023-00181-4] [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: 08/24/2022] [Accepted: 07/05/2023] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND Glioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression. METHODS Gene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene's expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM. RESULTS Ten key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM. CONCLUSIONS The proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.
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Affiliation(s)
- Seema Sandeep Redekar
- Pillai College of Engineering, New Panvel, Mumbai, India.
- SIES Graduate School of Technology, Navi Mumbai, Mumbai, India.
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Lu C, Xing ZX, Xia XG, Long ZD, Chen B, Zhou P, Wang R. Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma. World J Gastrointest Oncol 2023; 15:1241-1252. [PMID: 37546550 PMCID: PMC10401473 DOI: 10.4251/wjgo.v15.i7.1241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/14/2023] [Accepted: 06/12/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.
AIM To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.
METHODS We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.
RESULTS Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively.
CONCLUSION Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.
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Affiliation(s)
- Chao Lu
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Zhi-Xiang Xing
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Xi-Gang Xia
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Zhi-Da Long
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Bo Chen
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Peng Zhou
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
| | - Rui Wang
- Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
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Mozaffari J, Amirkhani A, Shokouhi SB. A survey on deep learning models for detection of COVID-19. Neural Comput Appl 2023; 35:1-29. [PMID: 37362568 PMCID: PMC10224665 DOI: 10.1007/s00521-023-08683-x] [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: 10/14/2021] [Accepted: 05/10/2023] [Indexed: 06/28/2023]
Abstract
The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08683-x.
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Affiliation(s)
- Javad Mozaffari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Abdollah Amirkhani
- School of Automotive Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
| | - Shahriar B. Shokouhi
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, 16846-13114 Iran
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Xia J, Chen M, Fang W. Air Combat Intention Recognition with Incomplete Information Based on Decision Tree and GRU Network. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040671. [PMID: 37190459 PMCID: PMC10138048 DOI: 10.3390/e25040671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/24/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023]
Abstract
Battlefield information is generally incomplete, uncertain, or deceptive. To realize enemy intention recognition in an uncertain and incomplete air combat information environment, a novel intention recognition method is proposed. After repairing the missing state data of an enemy fighter, the gated recurrent unit (GRU) network, supplemented by the highest frequency method (HFM), is used to predict the future state of enemy fighter. An intention decision tree is constructed to extract the intention classification rules from the incomplete a priori knowledge, where the decision support degree of attributes is introduced to determine the node-splitting sequence according to the information entropy of partitioning (IEP). Subsequently, the enemy fighter intention is recognized based on the established intention decision tree and the predicted state data. Furthermore, a target maneuver tendency function is proposed to screen out the possible deceptive attack intention. The one-to-one air combat simulation shows that the proposed method has advantages in both accuracy and efficiency of state prediction and intention recognition, and is suitable for enemy fighter intention recognition in small air combat situations.
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Affiliation(s)
- Jingyang Xia
- School of Management, Wuhan University of Science and Technology, Wuhan 430081, China
| | - Mengqi Chen
- School of Advanced Interdisciplinary Studies, Hunan University of Technology and Business, Changsha 410205, China
| | - Weiguo Fang
- School of Economics and Management, Beihang University, Beijing 100191, China
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Mehrpour O, Saeedi F, Nakhaee S, Tavakkoli Khomeini F, Hadianfar A, Amirabadizadeh A, Hoyte C. Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System. BMC Med Inform Decis Mak 2023; 23:60. [PMID: 37024869 PMCID: PMC10080923 DOI: 10.1186/s12911-022-02095-y] [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: 03/30/2022] [Accepted: 12/26/2022] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases. METHODS The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance. RESULTS Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%. CONCLUSION Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.
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Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, TX, USA.
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, Birjand, Iran.
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, Birjand, Iran
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, Birjand, Iran
| | | | - Ali Hadianfar
- Department of Epidemiology and Biostatistics, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Amirabadizadeh
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Prediction of Coronary Artery Disease Using Machine Learning Techniques with Iris Analysis. Diagnostics (Basel) 2023; 13:diagnostics13061081. [PMID: 36980389 PMCID: PMC10046987 DOI: 10.3390/diagnostics13061081] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 02/17/2023] [Accepted: 02/24/2023] [Indexed: 03/18/2023] Open
Abstract
Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and narrowed, limiting blood flow to the heart muscles. It is the most common type of heart disease and has the highest mortality rate. Early diagnosis of CAD can prevent the disease from progressing and can make treatment easier. Optimal treatment, in addition to the early detection of CAD, can improve the prognosis for these patients. This study proposes a new method for non-invasive diagnosis of CAD using iris images. In this study, iridology, a method of analyzing the iris to diagnose health conditions, was combined with image processing techniques to detect the disease in a total of 198 volunteers, 94 with CAD and 104 without. The iris was transformed into a rectangular format using the integral differential operator and the rubber sheet methods, and the heart region was cropped according to the iris map. Features were extracted using wavelet transform, first-order statistical analysis, a Gray-Level Co-Occurrence Matrix (GLCM), and a Gray Level Run Length Matrix (GLRLM). The model’s performance was evaluated based on accuracy, sensitivity, specificity, precision, score, mean, and Area Under the Curve (AUC) metrics. The proposed model has a 93% accuracy rate for predicting CAD using the Support Vector Machine (SVM) classifier. With the proposed method, coronary artery disease can be preliminarily diagnosed by iris analysis without needing electrocardiography, echocardiography, and effort tests. Additionally, the proposed method can be easily used to support telediagnosis applications for coronary artery disease in integrated telemedicine systems.
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Alam Suha S, Islam MN. Exploring the Dominant Features and Data-driven Detection of Polycystic Ovary Syndrome through Modified Stacking Ensemble Machine Learning Technique. Heliyon 2023; 9:e14518. [PMID: 36994397 PMCID: PMC10040521 DOI: 10.1016/j.heliyon.2023.e14518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 01/09/2023] [Accepted: 03/09/2023] [Indexed: 03/18/2023] Open
Abstract
Polycystic ovary syndrome (PCOS) is the most frequent endocrinological anomaly in reproductive women that causes persistent hormonal secretion disruption, leading to the formation of numerous cysts within the ovaries and serious health complications. But the real-world clinical detection technique for PCOS is very critical since the accuracy of interpretations being substantially dependent on the physician's expertise. Thus, an artificially intelligent PCOS prediction model might be a feasible additional technique to the error prone and time-consuming diagnostic technique. In this study, a modified ensemble machine learning (ML) classification approach is proposed utilizing state-of-the-art stacking technique for PCOS identification with patients' symptom data; employing five traditional ML models as base learners and then one bagging or boosting ensemble ML model as the meta-learner of the stacked model. Furthermore, three distinct types of feature selection strategies are applied to pick different sets of features with varied numbers and combinations of attributes. To evaluate and explore the dominant features necessary for predicting PCOS, the proposed technique with five variety of models and other ten types of classifiers is trained, tested and assessed utilizing different feature sets. As outcomes, the proposed stacking ensemble technique significantly enhances the accuracy in comparison to the other existing ML based techniques in case of all varieties of feature sets. However, among various models investigated to categorize PCOS and non-PCOS patients, the stacking ensemble model with 'Gradient Boosting' classifier as meta learner outperforms others with 95.7% accuracy while utilizing the top 25 features selected using Principal Component Analysis (PCA) feature selection technique.
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21
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Malek-Ahmadi M, Duff K, Chen K, Su Y, King JB, Koppelmans V, Schaefer SY. Volumetric regional MRI and neuropsychological predictors of motor task variability in cognitively unimpaired, Mild Cognitive Impairment, and probable Alzheimer's disease older adults. Exp Gerontol 2023; 173:112087. [PMID: 36639062 PMCID: PMC9974847 DOI: 10.1016/j.exger.2023.112087] [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: 09/22/2022] [Revised: 12/24/2022] [Accepted: 01/09/2023] [Indexed: 01/12/2023]
Abstract
INTRODUCTION The mechanisms linking motor function to Alzheimer's disease (AD) progression have not been well studied, despite evidence of AD pathology within motor brain regions. Thus, there is a need for new motor measure that is sensitive and specific to AD. METHODS In a sample of 121 older adults (54 cognitive unimpaired [CU], 35 amnestic Mild Cognitive Impairment [aMCI], and 32 probable mild AD), intrasubject standard deviation (ISD) across six trials of a novel upper-extremity motor task was predicted with volumetric regional gray matter and neuropsychological scores using classification and regression tree (CART) analyses. RESULTS Both gray matter and neuropsychological CART models indicated that motor task ISD (our measure of motor learning) was related to cortical regions and cognitive test scores associated with memory, executive function, and visuospatial skills. CART models also accurately distinguished motor task ISD of MCI and probable mild AD from CU. DISCUSSION Variability in motor task performance across practice trials may be valuable for understanding preclinical and early-stage AD.
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Affiliation(s)
- Michael Malek-Ahmadi
- Banner Alzheimer's Institute, Phoenix, AZ 85006, United States of America; Department of Biomedical Informatics, University of Arizona College of Medicine-Phoenix, Phoenix, AZ 85006, United States of America
| | - Kevin Duff
- Center for Alzheimer's Care, Imaging, & Research, University of Utah, Salt Lake City, UT 84108, United States of America
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ 85006, United States of America
| | - Yi Su
- Banner Alzheimer's Institute, Phoenix, AZ 85006, United States of America
| | - Jace B King
- Center for Alzheimer's Care, Imaging, & Research, University of Utah, Salt Lake City, UT 84108, United States of America
| | - Vincent Koppelmans
- Department of Psychiatry, University of Utah, Salt Lake City, UT 84108, United States of America
| | - Sydney Y Schaefer
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, United States of America.
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22
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Wang BG, Xu LL, Yang HY, Xie J, Xu G, Tang WC. Manual acupuncture for neuromusculoskeletal disorders: The selection of stimulation parameters and corresponding effects. Front Neurosci 2023; 17:1096339. [PMID: 36793537 PMCID: PMC9922711 DOI: 10.3389/fnins.2023.1096339] [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: 11/12/2022] [Accepted: 01/11/2023] [Indexed: 01/31/2023] Open
Abstract
As a minimally invasive method of physical stimulation, manual acupuncture (MA) is used globally as a sort of therapy for neuromusculoskeletal disorders. In addition to selecting appropriate acupoints, acupuncturists should also determine the stimulation parameters of needling, such as the manipulation (lifting-thrusting or twirling), needling amplitude, velocity, and stimulation time. At present, most studies focus on acupoint combination and mechanism of MA, the relationship between stimulation parameters and their therapeutic effects, as well as the influence on mechanism of action are relatively scattered, and lack of systematic summary and analysis. This paper reviewed the three types of stimulation parameters of MA, their common options and values, corresponding effects and potential mechanisms of action. The purpose of such efforts is to provide a useful reference for the dose-effect relationship of MA and the quantification and standardization of its clinical treatment of neuromusculoskeletal disorders to further promote the application of acupuncture in the world.
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Affiliation(s)
- Bing-Gan Wang
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Liu-Liu Xu
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hua-Yuan Yang
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian Xie
- Department of Acupuncture and Moxibustion, Yuhuan Hospital of Traditional Chinese Medicine, Taizhou, Zhejiang, China
| | - Gang Xu
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen-Chao Tang
- School of Acupuncture-Moxibustion and Tuina, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data. Diagnostics (Basel) 2023; 13:diagnostics13030481. [PMID: 36766587 PMCID: PMC9914433 DOI: 10.3390/diagnostics13030481] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
The biopsy is a gold standard method for tumor grading. However, due to its invasive nature, it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer-aided diagnosis (CAD) tool is required. Recently, many magnetic resonance imaging (MRI)-based CAD tools have been proposed for brain tumor grading. The MRI has several sequences, which can express tumor structure in different ways. However, a suitable MRI sequence for brain tumor classification is not yet known. The most common brain tumor is 'glioma', which is the most fatal form. Therefore, in the proposed study, to maximize the classification ability between low-grade versus high-grade glioma, three datasets were designed comprising three MRI sequences: T1-Weighted (T1W), T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Further, five well-established convolutional neural networks, AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 were adopted for tumor classification. An ensemble algorithm was proposed using the majority vote of above five deep learning (DL) models to produce more consistent and improved results than any individual model. Five-fold cross validation (K5-CV) protocol was adopted for training and testing. For the proposed ensembled classifier with K5-CV, the highest test accuracies of 98.88 ± 0.63%, 97.98 ± 0.86%, and 94.75 ± 0.61% were achieved for FLAIR, T2W, and T1W-MRI data, respectively. FLAIR-MRI data was found to be most significant for brain tumor classification, where it showed a 4.17% and 0.91% improvement in accuracy against the T1W-MRI and T2W-MRI sequence data, respectively. The proposed ensembled algorithm (MajVot) showed significant improvements in the average accuracy of three datasets of 3.60%, 2.84%, 1.64%, 4.27%, and 1.14%, respectively, against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50.
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Marques-Silva J, Ignatiev A. No silver bullet: interpretable ML models must be explained. Front Artif Intell 2023; 6:1128212. [PMID: 37168320 PMCID: PMC10165097 DOI: 10.3389/frai.2023.1128212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 03/29/2023] [Indexed: 05/13/2023] Open
Abstract
Recent years witnessed a number of proposals for the use of the so-called interpretable models in specific application domains. These include high-risk, but also safety-critical domains. In contrast, other works reported some pitfalls of machine learning model interpretability, in part justified by the lack of a rigorous definition of what an interpretable model should represent. This study proposes to relate interpretability with the ability of a model to offer explanations of why a prediction is made given some point in feature space. Under this general goal of offering explanations to predictions, this study reveals additional limitations of interpretable models. Concretely, this study considers application domains where the purpose is to help human decision makers to understand why some prediction was made or why was not some other prediction made, and where irreducible (and so minimal) information is sought. In such domains, this study argues that answers to such why (or why not) questions can exhibit arbitrary redundancy, i.e., the answers can be simplified, as long as these answers are obtained by human inspection of the interpretable ML model representation.
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Affiliation(s)
| | - Alexey Ignatiev
- Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Melbourne, VIC, Australia
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25
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Huang MJ, Ye L, Yu KX, Liu J, Li K, Wang XD, Li JP. Development of prediction model of low anterior resection syndrome for colorectal cancer patients after surgery based on machine-learning technique. Cancer Med 2023; 12:1501-1519. [PMID: 35899858 PMCID: PMC9883536 DOI: 10.1002/cam4.5041] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 06/18/2022] [Accepted: 07/07/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Low anterior resection syndrome (LARS) is a common postoperative complication in patients with colorectal cancer, which seriously affects their postoperative quality of life. At present, the aetiology of LARS is still unclear, but some risk factors have been studied. Accurate prediction and early management of medical intervention are keys to improving the quality of life of such high-risk patients. OBJECTIVES Based on machine-learning methods, this study used the follow-up results of postoperative patients with colorectal cancer to develop prediction models for LARS and conducted a comparative analysis between the different models. METHODS A total of 382 patients diagnosed with colorectal cancer and undergoing surgery at West China Hospital from April 2017 to December 2020 were retrospectively selected as the development cohort. Logistic regression, support vector machine, decision tree, random forest and artificial neural network algorithms were used to construct the prediction models of the obtained dataset. The models were internally validated using cross-validation. The area under the curve and Brier score measures were used to evaluate and compare the differentiation and calibration degrees of the models. The sensitivity, specificity, positive predictive value and negative predictive value of the different models were described for clinical use. RESULTS A total of 342 patients were included, the incidence of LARS being 47.4% (162/342) during the six-month follow-up. After feature selection, the factors influencing the occurrence of LARS were found to be location, distance, diverting stoma, exsufflation and surgical type. The prediction models based on five machine-learning methods all showed acceptable performance. CONCLUSIONS The five models developed based on the machine-learning methods showed good prediction performance. However, considering the simplicity of clinical use of the model results, the logistic regression model is most recommended. The clinical applicability of these models will also need to be evaluated with external cohort data.
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Affiliation(s)
- Ming Jun Huang
- West China School of Nursing/Day Surgery Center, West China Hospital, Sichuan University, Chengdu, China
| | - Lin Ye
- West China School of Stomatology, Sichuan University, Chengdu, China.,Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ke Xin Yu
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.,West China Medical School, Sichuan University, Chengdu, China
| | - Jing Liu
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.,West China School of Nursing/West China Hospital, Sichuan University, Chengdu, China
| | - Ka Li
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, China
| | - Xiao Dong Wang
- Division of Gastrointestinal Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Ji Ping Li
- Nursing Department, West China Hospital, Sichuan University, Chengdu, China
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Jin J, Zhou H, Sun S, Tian Z, Ren H, Feng J, Jiang X. Machine learning based gray-level co-occurrence matrix early warning system enables accurate detection of colorectal cancer pelvic bone metastases on MRI. Front Oncol 2023; 13:1121594. [PMID: 37035167 PMCID: PMC10073745 DOI: 10.3389/fonc.2023.1121594] [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: 12/16/2022] [Accepted: 03/02/2023] [Indexed: 04/11/2023] Open
Abstract
Objective The mortality of colorectal cancer patients with pelvic bone metastasis is imminent, and timely diagnosis and intervention to improve the prognosis is particularly important. Therefore, this study aimed to build a bone metastasis prediction model based on Gray level Co-occurrence Matrix (GLCM) - based Score to guide clinical diagnosis and treatment. Methods We retrospectively included 614 patients with colorectal cancer who underwent pelvic multiparameter magnetic resonance image(MRI) from January 2015 to January 2022 in the gastrointestinal surgery department of Gezhouba Central Hospital of Sinopharm. GLCM-based Score and Machine learning algorithm, that is,artificial neural net7work model(ANNM), random forest model(RFM), decision tree model(DTM) and support vector machine model(SVMM) were used to build prediction model of bone metastasis in colorectal cancer patients. The effectiveness evaluation of each model mainly included decision curve analysis(DCA), area under the receiver operating characteristic (AUROC) curve and clinical influence curve(CIC). Results We captured fourteen categories of radiomics data based on GLCM for variable screening of bone metastasis prediction models. Among them, Haralick_90, IV_0, IG_90, Haralick_30, CSV, Entropy and Haralick_45 were significantly related to the risk of bone metastasis, and were listed as candidate variables of machine learning prediction models. Among them, the prediction efficiency of RFM in combination with Haralick_90, Haralick_all, IV_0, IG_90, IG_0, Haralick_30, CSV, Entropy and Haralick_45 in training set and internal verification set was [AUC: 0.926,95% CI: 0.873-0.979] and [AUC: 0.919,95% CI: 0.868-0.970] respectively. The prediction efficiency of the other four types of prediction models was between [AUC: 0.716,95% CI: 0.663-0.769] and [AUC: 0.912,95% CI: 0.859-0.965]. Conclusion The automatic segmentation model based on diffusion-weighted imaging(DWI) using depth learning method can accurately segment the pelvic bone structure, and the subsequently established radiomics model can effectively detect bone metastases within the pelvic scope, especially the RFM algorithm, which can provide a new method for automatically evaluating the pelvic bone turnover of colorectal cancer patients.
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Sharifkazemi M, Hooshanginezhad Z, Zoroufian A, Shamsa K. Is it the Time to Move Towards Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve Guided Percutaneous Coronary Intervention? The Pros and Cons. Curr Cardiol Rev 2023; 19:e190123212887. [PMID: 36658709 PMCID: PMC10494271 DOI: 10.2174/1573403x19666230119115228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 11/08/2022] [Accepted: 11/21/2022] [Indexed: 01/21/2023] Open
Abstract
Coronary artery disease is the leading cause of mortality worldwide. Diagnosis is conventionally performed by direct visualization of the arteries by invasive coronary angiography (ICA), which has inherent limitations and risks. Measurement of fractional flow reserve (FFR) has been suggested for a more accurate assessment of ischemia in the coronary artery with high accuracy for determining the severity and decision on the necessity of intervention. Nevertheless, invasive coronary angiography-derived fractional flow reserve (ICA-FFR) is currently used in less than one-third of clinical practices because of the invasive nature of ICA and the need for additional equipment and experience, as well as the cost and extra time needed for the procedure. Recent technical advances have moved towards non-invasive high-quality imaging modalities, such as magnetic resonance, single-photon emission computed tomography, and coronary computed tomography (CT) scan; however, none had a definitive modality to confirm hemodynamically significant coronary artery stenosis. Coronary computed tomography angiography (CCTA) can provide accurate anatomic and hemodynamic data about the coronary lesion, especially calculating fractional flow reserve derived from CCTA (CCTA-FFR). Although growing evidence has been published regarding CCTA-FFR results being comparable to ICA-FFR, CCTA-FFR has not yet replaced the invasive conventional angiography, pending additional studies to validate the advantages and disadvantages of each diagnostic method. Furthermore, it has to be identified whether revascularization of a stenotic lesion is plausible based on CCTA-FFR and if the therapeutic plan can be determined safely and accurately without confirmation from invasive methods. Therefore, in the present review, we will outline the pros and cons of using CCTA-FFR vs. ICA-FFR regarding diagnostic accuracy and treatment decision-making.
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Affiliation(s)
| | - Zahra Hooshanginezhad
- Division of Cardiology, Nemazee Hospital, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Arezou Zoroufian
- Division of Cardiology, Tehran Heart Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kamran Shamsa
- Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
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Reddy KVV, Elamvazuthi I, Aziz AA, Paramasivam S, Chua HN, Pranavanand S. An Efficient Prediction System for Coronary Heart Disease Risk Using Selected Principal Components and Hyperparameter Optimization. APPLIED SCIENCES 2022; 13:118. [DOI: 10.3390/app13010118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Medical science-related studies have reinforced that the prevalence of coronary heart disease which is associated with the heart and blood vessels has been the most significant cause of health loss and death globally. Recently, data mining and machine learning have been used to detect diseases based on the unique characteristics of a person. However, these techniques have often posed challenges due to the complexity in understanding the objective of the datasets, the existence of too many factors to analyze as well as lack of performance accuracy. This research work is of two-fold effort: firstly, feature extraction and selection. This entails extraction of the principal components, and consequently, the Correlation-based Feature Selection (CFS) method was applied to select the finest principal components of the combined (Cleveland and Statlog) heart dataset. Secondly, by applying datasets to three single and three ensemble classifiers, the best hyperparameters that reflect the pre-eminent predictive outcomes were investigated. The experimental result reveals that hyperparameter optimization has improved the accuracy of all the models. In the comparative studies, the proposed work outperformed related works with an accuracy of 97.91%, and an AUC of 0.996 by employing six optimal principal components selected from the CFS method and optimizing parameters of the Rotation Forest ensemble classifier.
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Sayadi M, Varadarajan V, Sadoughi F, Chopannejad S, Langarizadeh M. A Machine Learning Model for Detection of Coronary Artery Disease Using Noninvasive Clinical Parameters. LIFE (BASEL, SWITZERLAND) 2022; 12:life12111933. [PMID: 36431068 PMCID: PMC9698583 DOI: 10.3390/life12111933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 11/22/2022]
Abstract
Background and Objective: Coronary artery disease (CAD) is one of the most prevalent causes of death worldwide. The early diagnosis and timely medical care of cardiovascular patients can greatly prevent death and reduce the cost of treatments associated with CAD. In this study, we attempt to prepare a new model for early CAD diagnosis. The proposed model can diagnose CAD based on clinical data and without the use of an invasive procedure. Methods: In this paper, machine-learning (ML) techniques were used for the early detection of CAD, which were applied to a CAD dataset known as Z-Alizadeh Sani. Since this dataset has 54 features, the Pearson correlation feature selection method was conducted to identify the most effective features. Then, six machine learning techniques including decision tree, deep learning, logistic regression, random forest, support vector machine (SVM), and Xgboost were employed based on a semi-random-partitioning framework. Result: Applying Pearson feature selection to the dataset demonstrated that only eight features were the most effective for CAD diagnosis. The results of running the six machine-learning models on the selected features showed that logistic regression and SVM had the same performance with 95.45% accuracy, 95.91% sensitivity, 91.66% specificity, and a 96.90% F1 score. In addition, the ROC curve indicates a similar result regarding the AUC (0.98). Conclusions: Prediction is an important component of medical decision support systems. The results of the present study showed that feature selection has a high impact on machine-learning performance and, regardless of the evaluation metrics of the machine-learning models, determining the effective features is very important. However, SVM and Logistic Regression were designated as the best models according to our selected features.
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Affiliation(s)
- Mohammadjavad Sayadi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Department of Computer Engineering, Technical and Vocational University (TVU), Tehran 14357-61137, Iran
| | - Vijayakumar Varadarajan
- School of Computer Science and Engineering, The University of New South Wales, Sydney 2052, Australia
- Dean International, Ajeenkya D Y Patil University, Pune 412105, India
- Swiss School of Business and Management, 1213 Geneva, Switzerland
- Correspondence: (V.V.); (M.L.)
| | - Farahnaz Sadoughi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Sara Chopannejad
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Mostafa Langarizadeh
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Correspondence: (V.V.); (M.L.)
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30
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Wu HY, Li ZG, Sun XK, Bai WM, Wang AD, Ma YC, Diao RH, Fan EY, Zhao F, Liu YQ, Hong YZ, Guo MH, Xue H, Liang WB. Predicting willingness to donate blood based on machine learning: two blood donor recruitments during COVID-19 outbreaks. Sci Rep 2022; 12:19165. [PMID: 36357435 PMCID: PMC9647248 DOI: 10.1038/s41598-022-21215-2] [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: 05/04/2022] [Accepted: 09/23/2022] [Indexed: 11/11/2022] Open
Abstract
Machine learning methods are a novel way to predict and rank donors' willingness to donate blood and to achieve precision recruitment, which can improve the recruitment efficiency and meet the challenge of blood shortage. We collected information about experienced blood donors via short message service (SMS) recruitment and developed 7 machine learning-based recruitment models using PyCharm-Python Environment and 13 features which were described as a method for ranking and predicting donors' intentions to donate blood with a floating number between 0 and 1. Performance of the prediction models was assessed by the Area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score in the full dataset, and by the accuracy in the four sub-datasets. The developed models were applied to prospective validations of recruiting experienced blood donors during two COVID-19 pandemics, while the routine method was used as a control. Overall, a total of 95,476 recruitments via SMS and their donation results were enrolled in our modelling study. The strongest predictor features for the donation of experienced donors were blood donation interval, age, and donation frequency. Among the seven baseline models, the eXtreme Gradient Boosting (XGBoost) and Support vector machine models (SVM) achieved the best performance: mean (95%CI) with the highest AUC: 0.809 (0.806-0.811), accuracy: 0.815 (0.812-0.818), precision: 0.840 (0.835-0.845), and F1 score of XGBoost: 0.843 (0.840-0.845) and recall of SVM: 0.991 (0.988-0.994). The hit rate of the XGBoost model alone and the combined XGBoost and SVM models were 1.25 and 1.80 times higher than that of the conventional method as a control in 2 recruitments respectively, and the hit rate of the high willingness to donate group was 1.96 times higher than that of the low willingness to donate group. Our results suggested that the machine learning models could predict and determine the experienced donors with a strong willingness to donate blood by a ranking score based on personalized donation data and demographical details, significantly improve the recruitment rate of blood donors and help blood agencies to maintain the blood supply in emergencies.
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Affiliation(s)
- Hong-yun Wu
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Zheng-gang Li
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Xin-kai Sun
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Wei-min Bai
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - An-di Wang
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Yu-chi Ma
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Ren-hua Diao
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Eng-yong Fan
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Fang Zhao
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
| | - Yun-qi Liu
- grid.499290.f0000 0004 6026 514XNanjing Foreign Language School, Nanjing, Jiangsu People’s Republic of China
| | - Yi-zhou Hong
- grid.499290.f0000 0004 6026 514XNanjing Foreign Language School, Nanjing, Jiangsu People’s Republic of China
| | - Ming-hua Guo
- Yangzhou Blood Station, Yangzhou, Jiangsu People’s Republic of China
| | - Hui Xue
- grid.263826.b0000 0004 1761 0489School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu People’s Republic of China
| | - Wen-biao Liang
- grid.488210.7Jiangsu Province Blood Center, Nanjing, Jiangsu People’s Republic of China
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Huang JD, Wang J, Ramsey E, Leavey G, Chico TJA, Condell J. Applying Artificial Intelligence to Wearable Sensor Data to Diagnose and Predict Cardiovascular Disease: A Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:8002. [PMID: 36298352 PMCID: PMC9610988 DOI: 10.3390/s22208002] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 06/06/2023]
Abstract
Cardiovascular disease (CVD) is the world's leading cause of mortality. There is significant interest in using Artificial Intelligence (AI) to analyse data from novel sensors such as wearables to provide an earlier and more accurate prediction and diagnosis of heart disease. Digital health technologies that fuse AI and sensing devices may help disease prevention and reduce the substantial morbidity and mortality caused by CVD worldwide. In this review, we identify and describe recent developments in the application of digital health for CVD, focusing on AI approaches for CVD detection, diagnosis, and prediction through AI models driven by data collected from wearables. We summarise the literature on the use of wearables and AI in cardiovascular disease diagnosis, followed by a detailed description of the dominant AI approaches applied for modelling and prediction using data acquired from sensors such as wearables. We discuss the AI algorithms and models and clinical applications and find that AI and machine-learning-based approaches are superior to traditional or conventional statistical methods for predicting cardiovascular events. However, further studies evaluating the applicability of such algorithms in the real world are needed. In addition, improvements in wearable device data accuracy and better management of their application are required. Lastly, we discuss the challenges that the introduction of such technologies into routine healthcare may face.
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Affiliation(s)
- Jian-Dong Huang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Jinling Wang
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Elaine Ramsey
- Department of Global Business & Enterprise, Ulster University at Magee, Londonderry BT48 7JL, UK
| | - Gerard Leavey
- School of Psychology, Ulster University at Coleraine, Londonderry BT52 1SA, UK
| | - Timothy J. A. Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK
| | - Joan Condell
- School of Computing, Engineering and Intelligent Systems, Ulster University at Magee, Londonderry BT48 7JL, UK
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Jung JY, Yang CM, Kim JJ. Decision Tree-Based Foot Orthosis Prescription for Patients with Pes Planus. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph191912484. [PMID: 36231782 PMCID: PMC9566258 DOI: 10.3390/ijerph191912484] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/02/2022] [Accepted: 09/29/2022] [Indexed: 05/27/2023]
Abstract
Pes planus, one of the most common foot deformities, includes the loss of the medial arch, misalignment of the rearfoot, and abduction of the forefoot, which negatively affects posture and gait. Foot orthosis, which is effective in normalizing the arch and providing stability during walking, is prescribed for the purpose of treatment and correction. Currently, machine learning technology for classifying and diagnosing foot types is being developed, but it has not yet been applied to the prescription of foot orthosis for the treatment and management of pes planus. Thus, the aim of this study is to propose a model that can prescribe a customized foot orthosis to patients with pes planus by learning from and analyzing various clinical data based on a decision tree algorithm called classification and regressing tree (CART). A total of 8 parameters were selected based on the feature importance, and 15 rules for the prescription of foot orthosis were generated. The proposed model based on the CART algorithm achieved an accuracy of 80.16%. This result suggests that the CART model developed in this study can provide adequate help to clinicians in prescribing foot orthosis easily and accurately for patients with pes planus. In the future, we plan to acquire more clinical data and develop a model that can prescribe more accurate and stable foot orthosis using various machine learning technologies.
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Affiliation(s)
- Ji-Yong Jung
- Division of Biomedical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, Korea
| | - Chang-Min Yang
- Department of Healthcare Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, Korea
| | - Jung-Ja Kim
- Division of Biomedical Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, Korea
- Research Center of Healthcare & Welfare Instrument for the Aged, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si 54896, Korea
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Classification of Parkinson's disease and its stages using machine learning. Sci Rep 2022; 12:14036. [PMID: 35982070 PMCID: PMC9388671 DOI: 10.1038/s41598-022-18015-z] [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: 01/18/2022] [Accepted: 08/03/2022] [Indexed: 11/19/2022] Open
Abstract
As digital health technology becomes more pervasive, machine learning (ML) provides a robust way to analyze and interpret the myriad of collected features. The purpose of this preliminary work was to use ML classification to assess the benefits and relevance of neurocognitive features both tablet-based assessments and self-reported metrics, as they relate to Parkinson’s Disease (PD) and its stages [Hoehn and Yahr (H&Y) Stages 1–5]. Further, this work aims to compare perceived versus sensor-based neurocognitive abilities. In this study, 75 participants (\documentclass[12pt]{minimal}
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\begin{document}$$n = 25$$\end{document}n=25 control) completed 14 tablet-based neurocognitive functional tests (e.g., motor, memory, speech, executive, and multifunction), functional movement assessments (e.g., Berg Balance Scale), and standardized health questionnaires (e.g., PDQ-39). Decision tree classification of sensor-based features allowed for the discrimination of PD from healthy controls with an accuracy of \documentclass[12pt]{minimal}
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\begin{document}$$92.6\%$$\end{document}92.6%, and early and advanced stages of PD with an accuracy of \documentclass[12pt]{minimal}
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\begin{document}$$73.7\%$$\end{document}73.7%; compared to the current gold standard tools [e.g., standardized health questionnaires (\documentclass[12pt]{minimal}
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\begin{document}$$78.3\%$$\end{document}78.3% accuracy) and functional movement assessments (\documentclass[12pt]{minimal}
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\begin{document}$$70\%$$\end{document}70% accuracy)]. Significant features were also identified using decision tree classification. Device magnitude of acceleration was significant in 12 of 14 tests (\documentclass[12pt]{minimal}
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\begin{document}$$85.7\%$$\end{document}85.7%), regardless of test type. For classification between diagnosed and control populations, 17 motor (e.g., device magnitude of acceleration), 9 accuracy (e.g., number of correct/incorrect interactions), and 8 timing features (e.g., time to between interactions) were significant. For classification between early (H&Y Stages 1 and 2) and advanced (H&Y Stages 3, 4, and 5) stages of PD, 7 motor, 12 accuracy, and 14 timing features were significant. Finally, this work depicts that perceived functionality of individuals with PD differed from sensor-based functionalities. In early-stage PD was shown to be \documentclass[12pt]{minimal}
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\begin{document}$$21.6\%$$\end{document}21.6% lower than sensor-based scores with notable perceived deficits in memory and executive function. However, individuals in advanced stages had elevated perceptions (1.57x) for executive and behavioral functions compared to early-stage populations. Machine learning in digital health systems allows for a more comprehensive understanding of neurodegenerative diseases and their stages and may also depict new features that influence the ways digital health technology should be configured.
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Ghane M, Ang MC, Nilashi M, Sorooshian S. Enhanced decision tree induction using evolutionary techniques for Parkinson's disease classification. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2022.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Chiu CC, Wu CM, Chien TN, Kao LJ, Qiu JT. Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques. Healthcare (Basel) 2022; 10:healthcare10061087. [PMID: 35742138 PMCID: PMC9222812 DOI: 10.3390/healthcare10061087] [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: 05/09/2022] [Revised: 06/07/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022] Open
Abstract
Predicting clinical patients’ vital signs is a leading critical issue in intensive care units (ICUs) related studies. Early prediction of the mortality of ICU patients can reduce the overall mortality and cost of complication treatment. Some studies have predicted mortality based on electronic health record (EHR) data by using machine learning models. However, the semi-structured data (i.e., patients’ diagnosis data and inspection reports) is rarely used in these models. This study utilized data from the Medical Information Mart for Intensive Care III. We used a Latent Dirichlet Allocation (LDA) model to classify text in the semi-structured data of some particular topics and established and compared the classification and regression trees (CART), logistic regression (LR), multivariate adaptive regression splines (MARS), random forest (RF), and gradient boosting (GB). A total of 46,520 ICU Patients were included, with 11.5% mortality in the Medical Information Mart for Intensive Care III group. Our results revealed that the semi-structured data (diagnosis data and inspection reports) of ICU patients contain useful information that can assist clinical doctors in making critical clinical decisions. In addition, in our comparison of five machine learning models (CART, LR, MARS, RF, and GB), the GB model showed the best performance with the highest area under the receiver operating characteristic curve (AUROC) (0.9280), specificity (93.16%), and sensitivity (83.25%). The RF, LR, and MARS models showed better performance (AUROC are 0.9096, 0.8987, and 0.8935, respectively) than the CART (0.8511). The GB model showed better performance than other machine learning models (CART, LR, MARS, and RF) in predicting the mortality of patients in the intensive care unit. The analysis results could be used to develop a clinically useful decision support system.
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Affiliation(s)
- Chih-Chou Chiu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Chung-Min Wu
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Te-Nien Chien
- College of Management, National Taipei University of Technology, Taipei 106, Taiwan
- Correspondence: ; Tel.: +886-2-2771-2171 (ext. 3403)
| | - Ling-Jing Kao
- Department of Business Management, National Taipei University of Technology, Taipei 106, Taiwan; (C.-C.C.); (C.-M.W.); (L.-J.K.)
| | - Jiantai Timothy Qiu
- Department of Obstetrics and Gynecology, Taipei Medical University Hospital, Taipei 110, Taiwan;
- College of Medicine, Taipei Medical University, Taipei 110, Taiwan
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Camerlingo N, Vettoretti M, Del Favero S, Facchinetti A, Choudhary P, Sparacino G. Generation of post-meal insulin correction boluses in type 1 diabetes simulation models for in-silico clinical trials: More realistic scenarios obtained using a decision tree approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106862. [PMID: 35597208 DOI: 10.1016/j.cmpb.2022.106862] [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: 02/04/2022] [Revised: 04/19/2022] [Accepted: 05/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) notably facilitate the design/testing of new therapies. Published simulation tools embed mathematical models of blood glucose (BG) and insulin dynamics, continuous glucose monitoring (CGM) sensors, and insulin treatments, but lack a realistic description of some aspects of patient lifestyle impacting on glucose control. Specifically, to effectively simulate insulin correction boluses, required to treat post-meal hyperglycemia (BG > 180 mg/dL), the timing of the bolus may be influenced by subjects' behavioral attitudes. In this work, we develop an easily interpretable model of the variability of correction bolus timing observed in real data, and embed it into a popular simulation tool for ISCTs. METHODS Using data collected in 196 adults with T1D monitored in free-living conditions, we trained a decision tree (DT) model to classify whether a correction bolus is injected in a future time window, based on predictors collected back in time, related to CGM data, previous insulin boluses and subject's characteristics. The performance was compared to that of a logistic regression classifier with LASSO regularization (LC), trained on the same dataset. After validation, the DT was embedded within a popular T1D simulation tool and an ISCT was performed to compare the simulated correction boluses against those observed in a subset of data not used for model training. RESULTS The DT provided better classification performance (accuracy: 0.792, sensitivity: 0.430, specificity: 0.878, precision: 0.455) than the LC and presented good interpretability. The most predictive features were related to CGM (and its temporal variations), time since the last insulin bolus, and time of the day. The correction boluses simulated by the DT, after implementation in the simulation tool, showed a good agreement with real-world data. CONCLUSIONS The DT developed in this work represents a simple set of rules to mimic the same timing of correction boluses observed on real data. The inclusion of the model in simulation tools allows investigators to perform ISCTs that more realistically represent the patient behavior in taking correction boluses and the post-prandial BG response. In the future, more complex models can be investigated.
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Affiliation(s)
- N Camerlingo
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - M Vettoretti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - S Del Favero
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - A Facchinetti
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy
| | - P Choudhary
- Department of Diabetes, Leicester Diabetes Centre, University of Leicester, Gwendolen Rd, Leicester LE5 4PW, United Kingdom
| | - G Sparacino
- Department of Information Engineering, University of Padova, Via G. Gradenigo 6B, Padova 35131, Italy.
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Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm. ELECTRONICS 2022. [DOI: 10.3390/electronics11091495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.
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Jin Z, Li N. Diagnosis of each main coronary artery stenosis based on whale optimization algorithm and stacking model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:4568-4591. [PMID: 35430828 DOI: 10.3934/mbe.2022211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cardiovascular disease is currently one of the diseases with high morbidity and mortality worldwide. One of the main types is coronary artery disease (CAD), which occurs when one or more of the three main arteries, the left anterior descending (LAD) artery, the left circumflex (LCX) artery, and the right coronary artery (RCA), are narrowed. In this paper, we introduce a computer-aided diagnosis model, which uses the k-nearest neighbor (KNN)-based whale optimization algorithm (WOA) for feature selection and combines stacking model for CAD diagnosis and prediction. In WOA, the values in the solution vectors are all continuous, and a threshold is set for binary-conversion to obtain the optimal feature subsets of each main coronary artery. Then we develop a two-layer stacking model based on the selected feature subsets to diagnosis LAD, LCX and RCA. By the proposed method, we select 17 features for each main artery diagnosis, and the classification accuracy on LAD, LCX, and RCA test sets is 89.68, 88.71 and 85.81%, respectively. On the Z-Alizadeh Sani dataset, we compare the proposed feature selection method with other metaheuristics and compare the performance of WOA based on different wrappers. The experimental results show that, the KNN-based WOA method selects the optimal feature subsets, and the classification performance of the stacking model is better than other machine learning algorithms.
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Affiliation(s)
- Ziyu Jin
- College of Sciences, Northeastern University, Shenyang 110819, China
| | - Ning Li
- College of Sciences, Northeastern University, Shenyang 110819, China
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Haouassi H, Mahdaoui R, Chouhal O, Bakhouche A. An efficient classification rule generation for coronary artery disease diagnosis using a novel discrete equilibrium optimizer algorithm. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-213257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.
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Affiliation(s)
- Hichem Haouassi
- Department of Mathematics and Computer Science, ICOSI Lab, University Abbas Laghrour, Khenchela, Algeria
| | - Rafik Mahdaoui
- Department of Mathematics and Computer Science, ICOSI Lab, University Abbas Laghrour, Khenchela, Algeria
| | - Ouahiba Chouhal
- Department of Mathematics and Computer Science, ICOSI Lab, University Abbas Laghrour, Khenchela, Algeria
| | - Abdelali Bakhouche
- Department of Mathematics and Computer Science, ICOSI Lab, University Abbas Laghrour, Khenchela, Algeria
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Joloudari JH, Saadatfar H, GhasemiGol M, Alizadehsani R, Sani ZA, Hasanzadeh F, Hassannataj E, Sharifrazi D, Mansor Z. FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:3609-3635. [PMID: 35341267 DOI: 10.3934/mbe.2022167] [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] [Indexed: 06/14/2023]
Abstract
Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and fuzzy C-means clustering combined with deep neural network (FCM-DNN) are developed for diagnosing CAD on a cardiac magnetic resonance imaging (CMRI) dataset. The original dataset is used in two different approaches. First, the labeled dataset is applied to the NN and DNN to create the NN and DNN models. Second, the labels are removed, and the unlabeled dataset is clustered via the FCM method, and then, the clustered dataset is fed to the DNN to create the FCM-DNN model. By utilizing the second clustering and modeling, the training process is improved, and consequently, the accuracy is increased. As a result, the proposed FCM-DNN model achieves the best performance with a 99.91% accuracy specifying 10 clusters, i.e., 5 clusters for healthy subjects and 5 clusters for sick subjects, through the 10-fold cross-validation technique compared to the NN and DNN models reaching the accuracies of 92.18% and 99.63%, respectively. To the best of our knowledge, no study has been conducted for CAD diagnosis on the CMRI dataset using artificial intelligence methods. The results confirm that the proposed FCM-DNN model can be helpful for scientific and research centers.
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Affiliation(s)
| | - Hamid Saadatfar
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Mohammad GhasemiGol
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC 3216, Australia
| | - Zahra Alizadeh Sani
- Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Omid hospital, Iran University of Medical Sciences, Tehran, Iran
| | | | - Edris Hassannataj
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
| | - Danial Sharifrazi
- Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran
| | - Zulkefli Mansor
- Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi 43600, Malaysia
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Hassannataj Joloudari J, Azizi F, Nematollahi MA, Alizadehsani R, Hassannatajjeloudari E, Nodehi I, Mosavi A. GSVMA: A Genetic Support Vector Machine ANOVA Method for CAD Diagnosis. Front Cardiovasc Med 2022; 8:760178. [PMID: 35187099 PMCID: PMC8855497 DOI: 10.3389/fcvm.2021.760178] [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: 08/17/2021] [Accepted: 12/22/2021] [Indexed: 11/13/2022] Open
Abstract
Background Coronary artery disease (CAD) is one of the crucial reasons for cardiovascular mortality in middle-aged people worldwide. The most typical tool is angiography for diagnosing CAD. The challenges of CAD diagnosis using angiography are costly and have side effects. One of the alternative solutions is the use of machine learning-based patterns for CAD diagnosis. Methods Hence, this paper provides a new hybrid machine learning model called genetic support vector machine and analysis of variance (GSVMA). The analysis of variance (ANOVA) is known as the kernel function for the SVM algorithm. The proposed model is performed based on the Z-Alizadeh Sani dataset so that a genetic optimization algorithm is used to select crucial features. In addition, SVM with ANOVA, linear SVM (LSVM), and library for support vector machine (LIBSVM) with radial basis function (RBF) methods were applied to classify the dataset. Results As a result, the GSVMA hybrid method performs better than other methods. This proposed method has the highest accuracy of 89.45% through a 10-fold crossvalidation technique with 31 selected features on the Z-Alizadeh Sani dataset. Conclusion We demonstrated that SVM combined with genetic optimization algorithm could be lead to more accuracy. Therefore, our study confirms that the GSVMA method outperforms other methods so that it can facilitate CAD diagnosis.
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Affiliation(s)
| | - Faezeh Azizi
- Department of Computer Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
| | | | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation, Deakin University, Geelong, VIC, Australia
| | - Edris Hassannatajjeloudari
- Department of Nursing, School of Nursing and Allied Medical Sciences, Maragheh Faculty of Medical Sciences, Maragheh, Iran
| | - Issa Nodehi
- Department of Computer Engineering, University of Qom, Qom, Iran
| | - Amir Mosavi
- Faculty of Informatics, Technische Universität Dresden, Dresden, Germany
- Faculty of Civil Engineering, TU-Dresden, Dresden, Germany
- John von Neumann Faculty of Informatics, Óbuda University, Budapest, Hungary
- Institute of Information Society, University of Public Service, Budapest, Hungary
- Institute of Information Engineering, Automation and Mathematics, Slovak University of Technology in Bratislava, Bratislava, Slovakia
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Javeed A, Khan SU, Ali L, Ali S, Imrana Y, Rahman A. Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:9288452. [PMID: 35154361 PMCID: PMC8831075 DOI: 10.1155/2022/9288452] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/15/2022] [Indexed: 12/13/2022]
Abstract
One of the leading causes of deaths around the globe is heart disease. Heart is an organ that is responsible for the supply of blood to each part of the body. Coronary artery disease (CAD) and chronic heart failure (CHF) often lead to heart attack. Traditional medical procedures (angiography) for the diagnosis of heart disease have higher cost as well as serious health concerns. Therefore, researchers have developed various automated diagnostic systems based on machine learning (ML) and data mining techniques. ML-based automated diagnostic systems provide an affordable, efficient, and reliable solutions for heart disease detection. Various ML, data mining methods, and data modalities have been utilized in the past. Many previous review papers have presented systematic reviews based on one type of data modality. This study, therefore, targets systematic review of automated diagnosis for heart disease prediction based on different types of modalities, i.e., clinical feature-based data modality, images, and ECG. Moreover, this paper critically evaluates the previous methods and presents the limitations in these methods. Finally, the article provides some future research directions in the domain of automated heart disease detection based on machine learning and multiple of data modalities.
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Affiliation(s)
- Ashir Javeed
- Aging Research Center, Karolinska Institutet, Sweden
| | - Shafqat Ullah Khan
- Department of Electrical Engineering, University of Science and Technology Bannu, Pakistan
| | - Liaqat Ali
- Department of Electronics, University of Buner, Buner, Pakistan
| | - Sardar Ali
- School of Engineering and Applied Sciences, Isra University Islamabad Campus, Pakistan
| | - Yakubu Imrana
- School of Engineering, University of Development Studies, Tamale, Ghana
- School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China
| | - Atiqur Rahman
- Department of Computer Science, University of Science and Technology Bannu, Pakistan
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Fan X, Wang H, Zhao Y, Huang K, Wu Y, Sun T, Tsui K. Automatic fall risk assessment with Siamese network for stroke survivors using inertial sensor‐based signals. INT J INTELL SYST 2022. [DOI: 10.1002/int.22838] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Xiaomao Fan
- Department of Artificial Intelligence School of Computer Science South China Normal University Guangzhou China
| | - Hailiang Wang
- School of Design Hong Kong Polytechnic University Hong Kong SAR China
| | - Yang Zhao
- School of Public Health (Shenzhen) Sun Yat‐sen University Guangzhou China
| | - Kuang‐Hui Huang
- Tao‐Yuan General Hospital Ministry of Health and Welfare Taoyuan Taiwan region China
| | - Ya‐Ting Wu
- Tao‐Yuan General Hospital Ministry of Health and Welfare Taoyuan Taiwan region China
| | - Tien‐Lung Sun
- Department of Industrial Engineering and Management Yuan Ze University Taoyuan Taiwan region China
| | - Kwok‐Leung Tsui
- School of Data Science City University of Hong Kong Hong Kong SAR China
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Zare M, Zendehboudi S, Abdi MA. Deterministic tools to estimate induction time for methane hydrate formation in the presence of Luvicap 55 W solutions. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2021.118374] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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45
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Lin S, Lin Y, Wu K, Wang Y, Feng Z, Duan M, Liu S, Fan Y, Huang L, Zhou F. FeCO3, constructing the network biomarkers using the inter-feature correlation coefficients and its application in detecting high-order breast cancer biomarkers. Curr Bioinform 2022. [DOI: 10.2174/1574893617666220124123303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
This study aims to formulate the inter-feature correlation as the engineered features.
Background:
Modern biotechnologies tend to generate a huge number of characteristics of a sample, while an OMIC dataset usually has a few dozens or hundreds of samples due to the high costs of generating the OMIC data. So many bio-OMIC studies assumed the inter-feature independence and selected a feature with a high phenotype-association.
Objective:
However, many features are closely associated with each other due to their physical or functional interactions, which may be utilized as a new view of features.
Method:
This study proposed a feature engineering algorithm based on the correlation coefficients (FeCO3) by utilizing the correlations between a given sample and a few reference samples. A comprehensive evaluation was carried out for the proposed FeCO3 network features using 24 bio-OMIC datasets.
Result:
The experimental data suggested that the newly calculated FeCO3 network features tended to achieve better classification performances than the original features, using the same popular feature selection and classification algorithms. The FeCO3 network features were also consistently supported by the literature. FeCO3 was utilized to investigate the high-order engineered biomarkers of breast cancer, and detected the PBX2 gene (Pre-B-Cell Leukemia Transcription Factor 2) as one of the candidate breast cancer biomarkers. Although the two methylated residues cg14851325 (Pvalue=8.06e-2) and cg16602460 (Pvalue=1.19e-1) within PBX2 did not have statistically significant association with breast cancers, the high-order inter-feature correlations showed a significant association with breast cancers.
Conclusion:
The proposed FeCO3 network features calculated the high-order inter-feature correlations as novel features, and may facilitate the investigations of complex diseases from this new perspective. The source code is available in FigShare at 10.6084/m9.figshare.13550051 or the web site http://www.healthinformaticslab.org/supp/ .
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Affiliation(s)
- Shenggeng Lin
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- State Key Laboratory of Microbial Metabolism, and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yuqi Lin
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Kexin Wu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yueying Wang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin Province, China
| | - Zixuan Feng
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Meiyu Duan
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Shuai Liu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yusi Fan
- College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Lan Huang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
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Wadhawan S, Maini R. A Systematic Review on Prediction Techniques for Cardiac Disease. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGIES AND SYSTEMS APPROACH 2022. [DOI: 10.4018/ijitsa.290001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Mortality rate can be lowered with early prediction of cardiac diseases, which is one of the major issue in healthcare industry. In comparison of traditional methods, intelligent systems have potential to predict these diseases accurately at early stage even with complex data. Various intelligent DSS are presented by researchers for predicting this disease. To study the trends of these intelligent systems, to find the effective techniques for predicting cardiac disease and to find the future directions are the objective of this study. Therefore this paper presents a systematic review on state-of-art techniques based on ML, NN and FL. For analysis, we follow PRISMA statement and considered the studies presented from 2010 to 2020 from different databases. Analysis concluded that ML based techniques are broadly used for feature selection and classification and have the potential for the prediction of cardiac diseases. The future directions are to evaluate the rarely used prediction techniques and finding the way of improving them for model generalization with better prediction accuracy.
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Affiliation(s)
- Savita Wadhawan
- Department of CSE, Punjabi University, Patiala, India & MMICTBM, MM(DU), Mullana, Ambala, India
| | - Raman Maini
- Department of CSE, Punjabi University, Patiala, India
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Martin H, Morar U, Izquierdo W, Cabrerizo M, Cabrera A, Adjouadi M. Real-time frequency-independent single-Lead and single-beat myocardial infarction detection. Artif Intell Med 2021; 121:102179. [PMID: 34763801 DOI: 10.1016/j.artmed.2021.102179] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 07/29/2021] [Accepted: 09/21/2021] [Indexed: 11/26/2022]
Abstract
This study proposes a novel real-time frequency-independent myocardial infarction detector for Lead II electrocardiograms. The underlying Deep-LSTM network is trained using the PTB-XL database, the largest to date publicly available electrocardiography dataset, and is tested over the same and the older PTB database. By testing the model over distinct datasets, collected under different conditions and from different patients, a more realistic measure of the performance can be gauged from the deployed system. The detector is trained over 3589 myocardial infarction (MI) patients and 7115 healthy controls (HC) while it is evaluated on 1076 MIs and 1840 HCs. The proposed algorithm, achieved an accuracy of 77.12%, recall/sensitivity of 75.85%, and a specificity of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL validation set (fold 9), and 84.17%, 78.37%, 87.55% over the PTB-XL test set (fold 10). The model also achieves stable performance metrics over the frequency range of 202 Hz to 2.8 kHz. The processing time is dependent on the sampling frequency, ranging from 130 ms at 202 Hz to 1.8 s at 2.8 kHz. Such outcome is within the time required for real-time processing (less than 300 ms for fast heartbeats), between 202 Hz and 500 Hz making the algorithm practically real-time. Therefore, the proposed MI detector could be readily deployed onto existing wearable and/or portable devices and test instruments; potentially having significant societal and clinical impact in the lives of patients at risk for myocardial infarction.
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Affiliation(s)
- Harold Martin
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA.
| | - Ulyana Morar
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Walter Izquierdo
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Mercedes Cabrerizo
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | | | - Malek Adjouadi
- CATE, Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA
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48
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Arslan H. COVID-19 prediction based on genome similarity of human SARS-CoV-2 and bat SARS-CoV-like coronavirus. COMPUTERS & INDUSTRIAL ENGINEERING 2021; 161:107666. [PMID: 34511707 PMCID: PMC8423779 DOI: 10.1016/j.cie.2021.107666] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 07/13/2021] [Accepted: 09/05/2021] [Indexed: 05/03/2023]
Abstract
This paper proposes an efficient and accurate method to predict coronavirus disease 19 (COVID-19) based on the genome similarity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and a bat SARS-CoV-like coronavirus. We introduce similarity features to distinguish COVID-19 from other human coronaviruses by comparing human coronaviruses with a bat SARS-CoV-like coronavirus. In the proposed method each human coronavirus sequence is assigned to three similarity scores considering nucleotide similarities and mutations that lead to the strong absence of cytosine and guanine nucleotides. Next the proposed features are integrated with CpG island features of the genome sequences to improve COVID-19 prediction. Thus, each genome sequence is represented by five real numbers. We exhibit the effectiveness of the proposed features using six machine learning classifiers on a dataset including the genome sequences of human coronaviruses similar to SARS-CoV-2. The performances of the machine learning classifiers are close to each other and k-nearest neighbor classifier with similarity features achieves the best results with an accuracy of 99.2%. Moreover, k-nearest neighbor classifier with the integration of CpG based and similarity features has an admirable performance and achieves an accuracy of 99.8%. Experimental results demonstrate that similarity features remarkably decrease the number of false negatives and significantly improve the overall performance. The superiority of the proposed method is also highlighted by comparing with the state-of-the-art studies detecting COVID-19 from genome sequences.
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Affiliation(s)
- Hilal Arslan
- Department of Software Engineering, Ankara Yıldırım Beyazıt University, Turkey
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Application of Gene Expression Programming (GEP) in Modeling Hydrocarbon Recovery in WAG Injection Process. ENERGIES 2021. [DOI: 10.3390/en14217131] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Water alternating gas (WAG) injection has been successfully applied as a tertiary recovery technique. Forecasting WAG flooding performance using fast and robust models is of great importance to attain a better understanding of the process, optimize the operational conditions, and avoid high-cost blind tests in laboratory or pilot scales. In this study, we introduce a novel correlation to determine the performance of the near-miscible WAG flooding in strongly water-wet sandstones. We conduct dimensional analysis with Buckingham’s π theorem technique to generate dimensionless numbers using eight key parameters. Seven dimensionless numbers are employed as the input variables of the desired correlation for predicting the recovery factor of a near-miscible WAG injection. A verified mathematical model is used to generate the required training and testing data for the development of the correlation using a gene expression programming (GEP) algorithm. The provided data points are then separated into two subsets: training (67%) to develop the model and testing (33%) to assess the models’ capability. Conducting error analysis, statistical measures and graphical illustrations are provided to assess the effectiveness of the introduced model. The statistical analysis shows that the developed GEP-based correlation can generate target data with high precision such that the training phase leads to R2 = 92.85% and MSE = 1.38 × 10−3 and R2 = 91.93% and MSE = 4.30 × 10−3 are attained for the testing phase. The relative importance of the input dimensionless groups is also determined. According to the sensitivity analysis, decreasing the oil–water capillary number results in a significant reduction in RF in all cycles. Increasing the magnitudes of oil to gas viscosity ratio and oil to water viscosity ratio lowers the RF of each cycle. It is found that oil to gas viscosity ratio has a higher impact on RF value compared to oil to water viscosity ratio due to a higher viscosity gap between the gas and oil phases. It is expected that the GEP, as a fast and reliable tool, will be useful to find vital variables including relative permeability in complex transport phenomena such as three-phase flow in porous media.
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Comparison of Decision Tree and Long Short-Term Memory Approaches for Automated Foot Strike Detection in Lower Extremity Amputee Populations. SENSORS 2021; 21:s21216974. [PMID: 34770281 PMCID: PMC8587033 DOI: 10.3390/s21216974] [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: 09/01/2021] [Revised: 10/12/2021] [Accepted: 10/15/2021] [Indexed: 11/17/2022]
Abstract
Foot strike detection is important when evaluating a person’s gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.
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