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Yolandani, Liu D, Raynaldo FA, Dabbour M, Zhang X, Chen Z, Ding Q, Luo L, Ma H. Comparison of prediction models for soy protein isolate hydrolysates bitterness built using sensory, spectrofluorometric and chromatographic data from varying enzymes and degree of hydrolysis. Food Chem 2024; 442:138428. [PMID: 38241997 DOI: 10.1016/j.foodchem.2024.138428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/09/2023] [Accepted: 01/10/2024] [Indexed: 01/21/2024]
Abstract
The bitterness of soy protein isolate hydrolysates prepared using five proteases at varying degree of hydrolysis (DH) and its relation to physicochemical properties, i.e., surface hydrophobicity (H0), relative hydrophobicity (RH), and molecular weight (MW), were studied and developed for predictive modelling using machine learning. Bitter scores were collected from sensory analysis and assigned as the target, while the physicochemical properties were assigned as the features. The modelling involved data pre-processing with local outlier factor; model development with support vector machine, linear regression, adaptive boosting, and K-nearest neighbors algorithms; and performance evaluation by 10-fold stratified cross-validation. The results indicated that alcalase hydrolysates were the most bitter, followed by protamex, flavorzyme, papain, and bromelain. Distinctive correlation results were found among the physicochemical properties, influenced by the disparity of each protease. Among the features, the combination of RH-MW fitted various classification models and resulted in the best prediction performance.
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Affiliation(s)
- Yolandani
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China
| | - Dandan Liu
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China
| | - Fredy Agil Raynaldo
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China; College of Biosystems Engineering and Food Sciences, Zhejiang University, Hangzhou, People's Republic of China
| | - Mokhtar Dabbour
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China; Department of Agricultural and Biosystems Engineering, Faculty of Agriculture, Benha University, P.O. Box 13736, Moshtohor, Qaluobia, Egypt
| | - Xueli Zhang
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China
| | - Zhongyuan Chen
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China
| | - Qingzhi Ding
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China; Institute of Food Physical Processing, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Lin Luo
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China; Institute of Food Physical Processing, Jiangsu University, Zhenjiang 212013, People's Republic of China
| | - Haile Ma
- School of Food and Biological Engineering, Jiangsu University, 301 Xuefu Road, Zhenjiang 212013, People's Republic of China; Institute of Food Physical Processing, Jiangsu University, Zhenjiang 212013, People's Republic of China.
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da Silva Freitas L, de Moura FR, Buffarini R, Feás X, da Silva Júnior FMR. The relationship and consequences of venomous animal encounters in the context of climate change. Integr Environ Assess Manag 2024; 20:589-591. [PMID: 38639422 DOI: 10.1002/ieam.4919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 03/12/2024] [Indexed: 04/20/2024]
Affiliation(s)
| | - Fernando R de Moura
- Universidade Federal do Rio Grande-FURG, Rio Grande, Rio Grande do Sul, Brazil
| | - Romina Buffarini
- Universidade Federal do Rio Grande-FURG, Rio Grande, Rio Grande do Sul, Brazil
| | - Xesús Feás
- Academy of Veterinary Sciences of Galicia, Edificio EGAP, Santiago de Compostela, Spain
| | - Flavio M R da Silva Júnior
- Universidade Federal do Rio Grande-FURG, Rio Grande, Rio Grande do Sul, Brazil
- IEAM Editorial Board Member
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He Q, Ge S, Zeng S, Wang Y, Ye J, He Y, Li J, Wang Z, Guan T. Global attention based GNN with Bayesian collaborative learning for glomerular lesion recognition. Comput Biol Med 2024; 173:108369. [PMID: 38552283 DOI: 10.1016/j.compbiomed.2024.108369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 03/18/2024] [Accepted: 03/24/2024] [Indexed: 04/17/2024]
Abstract
BACKGROUND Glomerular lesions reflect the onset and progression of renal disease. Pathological diagnoses are widely regarded as the definitive method for recognizing these lesions, as the deviations in histopathological structures closely correlate with impairments in renal function. METHODS Deep learning plays a crucial role in streamlining the laborious, challenging, and subjective task of recognizing glomerular lesions by pathologists. However, the current methods treat pathology images as data in regular Euclidean space, limiting their ability to efficiently represent the complex local features and global connections. In response to this challenge, this paper proposes a graph neural network (GNN) that utilizes global attention pooling (GAP) to more effectively extract high-level semantic features from glomerular images. The model incorporates Bayesian collaborative learning (BCL), enhancing node feature fine-tuning and fusion during training. In addition, this paper adds a soft classification head to mitigate the semantic ambiguity associated with a purely hard classification. RESULTS This paper conducted extensive experiments on four glomerular datasets, comprising a total of 491 whole slide images (WSIs) and 9030 images. The results demonstrate that the proposed model achieves impressive F1 scores of 81.37%, 90.12%, 87.72%, and 98.68% on four private datasets for glomerular lesion recognition. These scores surpass the performance of the other models used for comparison. Furthermore, this paper employed a publicly available BReAst Carcinoma Subtyping (BRACS) dataset with an 85.61% F1 score to further prove the superiority of the proposed model. CONCLUSION The proposed model not only facilitates precise recognition of glomerular lesions but also serves as a potent tool for diagnosing kidney diseases effectively. Furthermore, the framework and training methodology of the GNN can be adeptly applied to address various pathology image classification challenges.
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Affiliation(s)
- Qiming He
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
| | - Shuang Ge
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Peng Cheng Laboratory, Shenzhen, China
| | - Siqi Zeng
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China; Greater Bay Area National Center of Technology Innovation, Guangzhou, China
| | - Yanxia Wang
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Jing Ye
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Yonghong He
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
| | - Jing Li
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China.
| | - Zhe Wang
- Department of Pathology, State Key Laboratory of Cancer Biology, Xijing Hospital, Fourth Military Medical University, Xi'an, China; School of Basic Medicine, Fourth Military Medical University, Xi'an, China
| | - Tian Guan
- Department of Life and Health, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, Guangdong, China
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Kosar A, Asif M, Ahmad MB, Akram W, Mahmood K, Kumari S. Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey. Artif Intell Med 2024; 151:102858. [PMID: 38583369 DOI: 10.1016/j.artmed.2024.102858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 01/02/2024] [Accepted: 03/25/2024] [Indexed: 04/09/2024]
Abstract
The unpredictable pandemic came to light at the end of December 2019, known as the novel coronavirus, also termed COVID-19, identified by the World Health Organization (WHO). The virus first originated in Wuhan (China) and rapidly affected most of the world's population. This outbreak's impact is experienced worldwide because it causes high mortality risk, many cases, and economic falls. Around the globe, the total number of cases and deaths reported till November 12, 2022, were >600 million and 6.6 million, respectively. During the period of COVID-19, several diverse diagnostic techniques have been proposed. This work presents a systematic review of COVID-19 diagnostic techniques in response to such acts. Initially, these techniques are classified into different categories based on their working principle and detection modalities, i.e. chest X-ray imaging, cough sound or respiratory patterns, RT-PCR, antigen testing, and antibody testing. After that, a comparative analysis is performed to evaluate these techniques' efficacy which may help to determine an optimum solution for a particular scenario. The findings of the proposed work show that Artificial Intelligence plays a vital role in developing COVID-19 diagnostic techniques which support the healthcare system. The related work can be a footprint for all the researchers, available under a single umbrella. Additionally, all the techniques are long-lasting and can be used for future pandemics.
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Affiliation(s)
- Amna Kosar
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Muhammad Asif
- Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
| | - Maaz Bin Ahmad
- College of Computing and Information Sciences, Karachi Institute of Economics and Technology (KIET), Karachi, Pakistan
| | - Waseem Akram
- Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC
| | - Khalid Mahmood
- Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliu, Taiwan, ROC.
| | - Saru Kumari
- Departement of Mathematics, Chaudhary Charan Singh University, Meerut, India
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Tao L, Zhou T, Wu Z, Hu F, Yang S, Kong X, Li C. ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein-DNA Interaction Hotspots. J Chem Inf Model 2024; 64:3548-3557. [PMID: 38587997 DOI: 10.1021/acs.jcim.3c02011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Protein-DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein-DNA interactions holds great significance for revealing the intricate mechanisms in protein-DNA recognition and for providing essential guidance for protein engineering. Aiming at protein-DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively.
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Affiliation(s)
- Lianci Tao
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Tong Zhou
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Zhixiang Wu
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Fangrui Hu
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Shuang Yang
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Xiaotian Kong
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
| | - Chunhua Li
- College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China
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Angulo-Aguado M, Carrillo-Martinez JC, Contreras-Bravo NC, Morel A, Parra-Abaunza K, Usaquén W, Fonseca-Mendoza DJ, Ortega-Recalde O. Next-generation sequencing of host genetics risk factors associated with COVID-19 severity and long-COVID in Colombian population. Sci Rep 2024; 14:8497. [PMID: 38605121 PMCID: PMC11009356 DOI: 10.1038/s41598-024-57982-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 03/24/2024] [Indexed: 04/13/2024] Open
Abstract
Coronavirus disease 2019 (COVID-19) was considered a major public health burden worldwide. Multiple studies have shown that susceptibility to severe infections and the development of long-term symptoms is significantly influenced by viral and host factors. These findings have highlighted the potential of host genetic markers to identify high-risk individuals and develop target interventions to reduce morbimortality. Despite its importance, genetic host factors remain largely understudied in Latin-American populations. Using a case-control design and a custom next-generation sequencing (NGS) panel encompassing 81 genetic variants and 74 genes previously associated with COVID-19 severity and long-COVID, we analyzed 56 individuals with asymptomatic or mild COVID-19 and 56 severe and critical cases. In agreement with previous studies, our results support the association between several clinical variables, including male sex, obesity and common symptoms like cough and dyspnea, and severe COVID-19. Remarkably, thirteen genetic variants showed an association with COVID-19 severity. Among these variants, rs11385942 (p < 0.01; OR = 10.88; 95% CI = 1.36-86.51) located in the LZTFL1 gene, and rs35775079 (p = 0.02; OR = 8.53; 95% CI = 1.05-69.45) located in CCR3 showed the strongest associations. Various respiratory and systemic symptoms, along with the rs8178521 variant (p < 0.01; OR = 2.51; 95% CI = 1.27-4.94) in the IL10RB gene, were significantly associated with the presence of long-COVID. The results of the predictive model comparison showed that the mixed model, which incorporates genetic and non-genetic variables, outperforms clinical and genetic models. To our knowledge, this is the first study in Colombia and Latin-America proposing a predictive model for COVID-19 severity and long-COVID based on genomic analysis. Our study highlights the usefulness of genomic approaches to studying host genetic risk factors in specific populations. The methodology used allowed us to validate several genetic variants previously associated with COVID-19 severity and long-COVID. Finally, the integrated model illustrates the importance of considering genetic factors in precision medicine of infectious diseases.
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Affiliation(s)
- Mariana Angulo-Aguado
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, D.C, Colombia
| | - Juan Camilo Carrillo-Martinez
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, D.C, Colombia
| | - Nora Constanza Contreras-Bravo
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, D.C, Colombia
| | - Adrien Morel
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, D.C, Colombia
| | | | - William Usaquén
- Populations Genetics and Identification Group, Institute of Genetics, Universidad Nacional de Colombia, Bogotá, D.C, Colombia
| | - Dora Janeth Fonseca-Mendoza
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, D.C, Colombia
| | - Oscar Ortega-Recalde
- School of Medicine and Health Sciences, Center for Research in Genetics and Genomics (CIGGUR), Institute of Translational Medicine (IMT), Universidad Del Rosario, Bogotá, D.C, Colombia.
- Departamento de Morfología, Facultad de Medicina e Instituto de Genética, Universidad Nacional de Colombia, Bogotá, D.C, Colombia.
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Yenikaya MA, Kerse G, Oktaysoy O. Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images. Front Public Health 2024; 12:1386110. [PMID: 38660365 PMCID: PMC11039909 DOI: 10.3389/fpubh.2024.1386110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. Methods In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. Results The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. Conclusion In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.
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Affiliation(s)
| | - Gökhan Kerse
- Faculty of Economics and Administrative Sciences, Department of Management Information Systems, Kafkas University, Kars, Türkiye
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Balamurugan M, Meera DS. Hybrid optimized temporal convolutional networks with long short-term memory for heart disease prediction with deep features. Comput Methods Biomech Biomed Engin 2024:1-25. [PMID: 38584483 DOI: 10.1080/10255842.2024.2310075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/10/2024] [Indexed: 04/09/2024]
Abstract
A heart attack is intended as top prevalent among all ruinous ailments. Day by day, the number of affected people count is increasing globally. The medical field is struggling to detect heart disease in the initial step. Early prediction can help patients to save their life. Thus, this paper implements a novel heart disease prediction model with the help of a hybrid deep learning strategy. The developed framework consists of various steps like (i) Data collection, (ii) Deep feature extraction, and (iii) Disease prediction. Initially, the standard medical data from various patients are acquired from the clinical standard datasets. Here, a One-Dimensional Convolutional Neural Network (1DCNN) is utilized for extracting the deep features from the acquired medical data to minimize the number of redundant data from the gathered large-scale data. The acquired deep features are directly fed to the Hybrid Optimized Deep Classifier (HODC) with the integration of Temporal Convolutional Networks (TCN) with Long Short-Term Memory (LSTM), where the parameters in both classifiers are optimized using the newly suggested Enhanced Forensic-Based Investigation (EFBI) inspired meta-optimization algorithm. Throughout the result analysis, the accuracy and precision rate of the offered approach is 98.67% and 99.48%. The evaluation outcomes show that the recommended system outperforms the extant systems in terms of performance metrics examination.
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Affiliation(s)
- M Balamurugan
- Research Scholar, Department of Computer Science and Engineering, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, India
| | - Dr S Meera
- Associate Professor, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Pallavaram, Chennai, Tamil Nadu, India
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Kulkarni C, Quraishi A, Raparthi M, Shabaz M, Khan MA, Varma RA, Keshta I, Soni M, Byeon H. Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer. BMC Med Inform Decis Mak 2024; 24:92. [PMID: 38575951 PMCID: PMC10996111 DOI: 10.1186/s12911-024-02495-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 03/29/2024] [Indexed: 04/06/2024] Open
Abstract
Emerging from the convergence of digital twin technology and the metaverse, consumer health (MCH) is witnessing a transformative shift. The amalgamation of bioinformatics with healthcare Big Data has ushered in a new era of disease prediction models that harness comprehensive medical data, enabling the anticipation of illnesses even before the onset of symptoms. In this model, deep neural networks stand out because they improve accuracy remarkably by increasing network depth and making weight changes using gradient descent. Nonetheless, traditional methods face their own set of challenges, including the issues of gradient instability and slow training. In this case, the Broad Learning System (BLS) stands out as a good alternative. It gets around the problems with gradient descent and lets you quickly rebuild a model through incremental learning. One problem with BLS is that it has trouble extracting complex features from complex medical data. This makes it less useful in a wide range of healthcare situations. In response to these challenges, we introduce DAE-BLS, a novel hybrid model that marries Denoising AutoEncoder (DAE) noise reduction with the efficiency of BLS. This hybrid approach excels in robust feature extraction, particularly within the intricate and multifaceted world of medical data. Validation using diverse datasets yields impressive results, with accuracies reaching as high as 98.50%. DAE-BLS's ability to rapidly adapt through incremental learning holds great promise for accurate and agile disease prediction, especially within the complex and dynamic healthcare scenarios of today.
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Affiliation(s)
- Chaitanya Kulkarni
- Department of Computer Engineering, Vidya Pratishthan's Kamalnayan Bajaj Institute of Engineering and Technology, Baramati, Pune, 413133, Maharashtra, India
| | - Aadam Quraishi
- M.D. Research, Intervention Treatment Institute, Houston, TX, USA
| | - Mohan Raparthi
- Software Engineer, Alphabet Life Science, Dallas, TX, 75063, USA
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology, Jammu, J&K, India.
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Raj A Varma
- Symbiosis Law School (SLS), Symbiosis International (Deemed University) (SIU), Vimannagar, Pune, Maharashtra, India
| | - Ismail Keshta
- Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh, Saudi Arabia
| | - Mukesh Soni
- Dr D Y Patil Vidyapeeth, Dr. D. Y. Patil School of Science and Technology, Pune, 411033, India
| | - Haewon Byeon
- Department of Digital Anti-Aging Healthcare, Inje University, Gimhae, Republic of Korea, 50834
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10
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Singh MS, Thongam K, Choudhary P, Bhagat PK. An Integrated Machine Learning Approach for Congestive Heart Failure Prediction. Diagnostics (Basel) 2024; 14:736. [PMID: 38611649 PMCID: PMC11011350 DOI: 10.3390/diagnostics14070736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/27/2024] [Accepted: 02/07/2024] [Indexed: 04/14/2024] Open
Abstract
Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision.
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Affiliation(s)
- M. Sheetal Singh
- Department of Computer Science and Engineering, National Institute of Technology Manipur, Langol, Imphal 795004, Manipur, India; (M.S.S.)
| | - Khelchandra Thongam
- Department of Computer Science and Engineering, National Institute of Technology Manipur, Langol, Imphal 795004, Manipur, India; (M.S.S.)
| | - Prakash Choudhary
- Department of Computer Science and Engineering, Central University of Rajasthan, Tehsil Kishangarh, Ajmer 305817, Rajasthan, India
| | - P. K. Bhagat
- Department of Computer Engineering and Applications, GLA University, Mathura 281406, Uttar Pradesh, India;
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11
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Chlabicz M, Nabożny A, Koszelew J, Łaguna W, Szpakowicz A, Sowa P, Budny W, Guziejko K, Róg-Makal M, Pancewicz S, Kondrusik M, Czupryna P, Cudowska B, Lebensztejn D, Moniuszko-Malinowska A, Wierzbicki A, Kamiński KA. Medical Misinformation in Polish on the World Wide Web During the COVID-19 Pandemic Period: Infodemiology Study. J Med Internet Res 2024; 26:e48130. [PMID: 38551638 PMCID: PMC10984342 DOI: 10.2196/48130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/26/2023] [Accepted: 11/29/2023] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Although researchers extensively study the rapid generation and spread of misinformation about the novel coronavirus during the pandemic, numerous other health-related topics are contaminating the internet with misinformation that have not received as much attention. OBJECTIVE This study aims to gauge the reach of the most popular medical content on the World Wide Web, extending beyond the confines of the pandemic. We conducted evaluations of subject matter and credibility for the years 2021 and 2022, following the principles of evidence-based medicine with assessments performed by experienced clinicians. METHODS We used 274 keywords to conduct web page searches through the BuzzSumo Enterprise Application. These keywords were chosen based on medical topics derived from surveys administered to medical practitioners. The search parameters were confined to 2 distinct date ranges: (1) January 1, 2021, to December 31, 2021; (2) January 1, 2022, to December 31, 2022. Our searches were specifically limited to web pages in the Polish language and filtered by the specified date ranges. The analysis encompassed 161 web pages retrieved in 2021 and 105 retrieved in 2022. Each web page underwent scrutiny by a seasoned doctor to assess its credibility, aligning with evidence-based medicine standards. Furthermore, we gathered data on social media engagements associated with the web pages, considering platforms such as Facebook, Pinterest, Reddit, and Twitter. RESULTS In 2022, the prevalence of unreliable information related to COVID-19 saw a noteworthy decline compared to 2021. Specifically, the percentage of noncredible web pages discussing COVID-19 and general vaccinations decreased from 57% (43/76) to 24% (6/25) and 42% (10/25) to 30% (3/10), respectively. However, during the same period, there was a considerable uptick in the dissemination of untrustworthy content on social media pertaining to other medical topics. The percentage of noncredible web pages covering cholesterol, statins, and cardiology rose from 11% (3/28) to 26% (9/35) and from 18% (5/28) to 26% (6/23), respectively. CONCLUSIONS Efforts undertaken during the COVID-19 pandemic to curb the dissemination of misinformation seem to have yielded positive results. Nevertheless, our analysis suggests that these interventions need to be consistently implemented across both established and emerging medical subjects. It appears that as interest in the pandemic waned, other topics gained prominence, essentially "filling the vacuum" and necessitating ongoing measures to address misinformation across a broader spectrum of health-related subjects.
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Affiliation(s)
- Małgorzata Chlabicz
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland
- Department of Invasive Cardiology, Medical University of Białystok, Białystok, Poland
| | - Aleksandra Nabożny
- Department of Software Engineering, Gdańsk University of Technology, Gdańsk, Poland
| | - Jolanta Koszelew
- R&D Department, Science4People Limited Liability Company, Szczecin, Poland
| | - Wojciech Łaguna
- Faculty of Computer Science, Bialystok University of Technology, Białystok, Poland
| | - Anna Szpakowicz
- Department of Cardiology, Medical University of Bialystok, Białystok, Poland
| | - Paweł Sowa
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland
| | - Wojciech Budny
- Department of Allergology and Internal Medicine, Medical University of Białystok, Białystok, Poland
| | - Katarzyna Guziejko
- 2nd Department of Lung Diseases and Tuberculosis, Medical University of Białystok, Białystok, Poland
| | - Magdalena Róg-Makal
- Department of Invasive Cardiology, Medical University of Białystok, Białystok, Poland
| | - Sławomir Pancewicz
- Department of Infectious Diseases and Neuroinfection, Medical University of Białystok, Białystok, Poland
| | - Maciej Kondrusik
- Department of Infectious Diseases and Neuroinfection, Medical University of Białystok, Białystok, Poland
| | - Piotr Czupryna
- Department of Infectious Diseases and Neuroinfection, Medical University of Białystok, Białystok, Poland
| | - Beata Cudowska
- Department of Pediatrics, Gastroenterology, Hepatology, Nutrition, Allergology and Pulmonology, Medical University of Bialystok, Białystok, Poland
| | - Dariusz Lebensztejn
- Department of Pediatrics, Gastroenterology, Hepatology, Nutrition, Allergology and Pulmonology, Medical University of Bialystok, Białystok, Poland
| | - Anna Moniuszko-Malinowska
- Department of Infectious Diseases and Neuroinfection, Medical University of Białystok, Białystok, Poland
| | - Adam Wierzbicki
- Department of Computer Science, Polish-Japaneese Academy of Information Technology, Warsaw, Poland
| | - Karol A Kamiński
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Białystok, Białystok, Poland
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Zhang S, Li H, Jing Q, Shen W, Luo W, Dai R. Anesthesia decision analysis using a cloud-based big data platform. Eur J Med Res 2024; 29:201. [PMID: 38528564 DOI: 10.1186/s40001-024-01764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/01/2024] [Indexed: 03/27/2024] Open
Abstract
Big data technologies have proliferated since the dawn of the cloud-computing era. Traditional data storage, extraction, transformation, and analysis technologies have thus become unsuitable for the large volume, diversity, high processing speed, and low value density of big data in medical strategies, which require the development of novel big data application technologies. In this regard, we investigated the most recent big data platform breakthroughs in anesthesiology and designed an anesthesia decision model based on a cloud system for storing and analyzing massive amounts of data from anesthetic records. The presented Anesthesia Decision Analysis Platform performs distributed computing on medical records via several programming tools, and provides services such as keyword search, data filtering, and basic statistics to reduce inaccurate and subjective judgments by decision-makers. Importantly, it can potentially to improve anesthetic strategy and create individualized anesthesia decisions, lowering the likelihood of perioperative complications.
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Affiliation(s)
- Shuiting Zhang
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Hui Li
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Qiancheng Jing
- Department of Otolaryngology Head and Neck Surgery, Hengyang Medical School, The Affiliated Changsha Central Hospital, University of South China, Changsha, 410000, Hunan, China
| | - Weiyun Shen
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Wei Luo
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China
| | - Ruping Dai
- Department of Anesthesiology, The Second Xiangya Hospital, Central South University, Changsha, 410008, Hunan, China.
- Anesthesia Medical Research, Center Central, South University, Changsha, 410008, Hunan, China.
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Hasan M, Sahid MA, Uddin MP, Marjan MA, Kadry S, Kim J. Performance discrepancy mitigation in heart disease prediction for multisensory inter-datasets. PeerJ Comput Sci 2024; 10:e1917. [PMID: 38660196 PMCID: PMC11041935 DOI: 10.7717/peerj-cs.1917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 02/12/2024] [Indexed: 04/26/2024]
Abstract
Heart disease is one of the primary causes of morbidity and death worldwide. Millions of people have had heart attacks every year, and only early-stage predictions can help to reduce the number. Researchers are working on designing and developing early-stage prediction systems using different advanced technologies, and machine learning (ML) is one of them. Almost all existing ML-based works consider the same dataset (intra-dataset) for the training and validation of their method. In particular, they do not consider inter-dataset performance checks, where different datasets are used in the training and testing phases. In inter-dataset setup, existing ML models show a poor performance named the inter-dataset discrepancy problem. This work focuses on mitigating the inter-dataset discrepancy problem by considering five available heart disease datasets and their combined form. All potential training and testing mode combinations are systematically executed to assess discrepancies before and after applying the proposed methods. Imbalance data handling using SMOTE-Tomek, feature selection using random forest (RF), and feature extraction using principle component analysis (PCA) with a long preprocessing pipeline are used to mitigate the inter-dataset discrepancy problem. The preprocessing pipeline builds on missing value handling using RF regression, log transformation, outlier removal, normalization, and data balancing that convert the datasets to more ML-centric. Support vector machine, K-nearest neighbors, decision tree, RF, eXtreme Gradient Boosting, Gaussian naive Bayes, logistic regression, and multilayer perceptron are used as classifiers. Experimental results show that feature selection and classification using RF produce better results than other combination strategies in both single- and inter-dataset setups. In certain configurations of individual datasets, RF demonstrates 100% accuracy and 96% accuracy during the feature selection phase in an inter-dataset setup, exhibiting commendable precision, recall, F1 score, specificity, and AUC score. The results indicate that an effective preprocessing technique has the potential to improve the performance of the ML model without necessitating the development of intricate prediction models. Addressing inter-dataset discrepancies introduces a novel research avenue, enabling the amalgamation of identical features from various datasets to construct a comprehensive global dataset within a specific domain.
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Affiliation(s)
- Mahmudul Hasan
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Md Abdus Sahid
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Md Palash Uddin
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
- School of Information Technology, Deakin University, Geelong, VIC, Australia
| | - Md Abu Marjan
- Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh
| | - Seifedine Kadry
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
- Department of Applied Data Science, Noroff University College, Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, Norway
- MEU Research Unit, Middle East University, Amman, Jordan
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan, Republic of South Korea
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14
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Qadri AM, Hashmi MSA, Raza A, Zaidi SAJ, Rehman AU. Heart failure survival prediction using novel transfer learning based probabilistic features. PeerJ Comput Sci 2024; 10:e1894. [PMID: 38660216 PMCID: PMC11042000 DOI: 10.7717/peerj-cs.1894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 01/30/2024] [Indexed: 04/26/2024]
Abstract
Heart failure is a complex cardiovascular condition characterized by the heart's inability to pump blood effectively, leading to a cascade of physiological changes. Predicting survival in heart failure patients is crucial for optimizing patient care and resource allocation. This research aims to develop a robust survival prediction model for heart failure patients using advanced machine learning techniques. We analyzed data from 299 hospitalized heart failure patients, addressing the issue of imbalanced data with the Synthetic Minority Oversampling (SMOTE) method. Additionally, we proposed a novel transfer learning-based feature engineering approach that generates a new probabilistic feature set from patient data using ensemble trees. Nine fine-tuned machine learning models are built and compared to evaluate performance in patient survival prediction. Our novel transfer learning mechanism applied to the random forest model outperformed other models and state-of-the-art studies, achieving a remarkable accuracy of 0.975. All models underwent evaluation using 10-fold cross-validation and tuning through hyperparameter optimization. The findings of this study have the potential to advance the field of cardiovascular medicine by providing more accurate and personalized prognostic assessments for individuals with heart failure.
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Affiliation(s)
- Azam Mehmood Qadri
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Muhammad Shadab Alam Hashmi
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Ali Raza
- Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Syed Ali Jafar Zaidi
- Institute of Information Technology, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Atiq ur Rehman
- Artificial Intelligence and Intelligent Systems Research Group, School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden
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15
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Barry KA, Manzali Y, Flouchi R, Balouki Y, Chelhi K, Elfar M. Exploring the use of association rules in random forest for predicting heart disease. Comput Methods Biomech Biomed Engin 2024; 27:338-346. [PMID: 36877167 DOI: 10.1080/10255842.2023.2185477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/07/2023] [Accepted: 02/16/2023] [Indexed: 03/07/2023]
Abstract
Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique.
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Affiliation(s)
| | | | - Rachid Flouchi
- Laboratory of Microbial Biotechnology and Bioactive Molecules, Science and Technologies Faculty, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Youssef Balouki
- Labo: Mathematics, Computer Science and Engineering Sciences(MISI), Settat, Morocco
| | - Khadija Chelhi
- The logistics center of excellence, Higher School of Textile and Clothing Industries(ESITH Casablanca), Casablanca, Morocco
| | - Mohamed Elfar
- LPAIS Laboratory, Faculty of Sciences, USMBA, Fez, Morocco
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16
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Rakusa M, Moro E, Akhvlediani T, Bereczki D, Bodini B, Cavallieri F, Fanciulli A, Filipović SR, Guekht A, Helbok R, Hochmeister S, Martinelli Boneschi F, Özturk S, Priori A, Romoli M, Willekens B, Zedde M, Sellner J. The COVID-19 pandemic and neurology: A survey on previous and continued restrictions for clinical practice, curricular training, and health economics. Eur J Neurol 2024; 31:e16168. [PMID: 38038262 DOI: 10.1111/ene.16168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/03/2023] [Accepted: 11/14/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND AND PURPOSE The COVID-19 pandemic has significantly impacted health systems worldwide. Here, we assessed the pandemic's impact on clinical service, curricular training, and financial burden from a neurological viewpoint during the enforced lockdown periods and the assumed recovery by 2023. METHODS An online 18-item survey was conducted by the European Academy of Neurology (EAN) NeuroCOVID-19 Task Force among the EAN community. The survey was online between February and March 2023. Questions related to general, demographic, clinical, work, education, and economic aspects. RESULTS We collected 430 responses from 79 countries. Most health care professionals were aged 35-44 years, with >15 years of work experience. The key findings of their observations were as follows. (i) Clinical services were cut back in all neurological subspecialties during the most restrictive COVID-19 lockdown period. The most affected neurological subspecialties were services for patients with dementia, and neuromuscular and movement disorders. The levels of reduction and the pace of recovery were distinct for acute emergencies and in- and outpatient care. Recovery was slow for sleep medicine, autonomic nervous system disorders, neurorehabilitation, and dementia care. (ii) Student and residency rotations and grand rounds were reorganized, and congresses were converted into a virtual format. Conferences are partly maintained in a hybrid format. (iii) Affordability of neurological care and medication shortage are emerging issues. CONCLUSIONS Recovery of neurological services up to spring 2023 has been incomplete following substantial disruption of neurological care, medical education, and health economics in the wake of the COVID-19 pandemic. The continued limitations for the delivery of neurological care threaten brain health and call for action on a global scale.
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Affiliation(s)
- Martin Rakusa
- Division of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | - Elena Moro
- Division of Neurology, CHU of Grenoble, Grenoble Institute of Neurosciences, INSERM U1216, Grenoble Alpes University, Grenoble, France
| | | | - Daniel Bereczki
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Benedetta Bodini
- Neurology Department, St. Antoine Hospital, APHP, Paris, France
- Paris Brain Institute, ICM, CNRS, INSERM, Sorbonne Université, Paris, France
| | - Francesco Cavallieri
- Neurology Unit, Neuromotor and Rehabilitation Department, Azienda USL-IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | | | - Saša R Filipović
- Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - Alla Guekht
- Research and Clinical Center for Neuropsychiatry, Moscow, Russian Federation
- Pirogov Russian National Research Medical University, Moscow, Russian Federation
| | - Raimund Helbok
- Department of Neurology, Johannes Kepler University, Linz, Austria
| | | | - Filippo Martinelli Boneschi
- Neurology Unit, ASST Santi Paolo e Carlo, Milan, Italy
- Department of Health Sciences, University of Milan, Milan, Italy
| | - Serefnur Özturk
- Department of Neurology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Alberto Priori
- Aldo Ravelli Center for Neurotechnology and Experimental Brain Therapeutics, Department of Health Sciences, University of Milan, Milan, Italy
- Clinical Neurology Unit, Azienda Socio-Sanitaria Territoriale Santi Paolo e Carlo and Department of Health Sciences, University of Milan, Milan, Italy
| | - Michele Romoli
- Neurology and Stroke Unit, Department of Neuroscience, Bufalini Hospital, Cesena, Italy
| | - Barbara Willekens
- Department of Neurology, Antwerp University Hospital, Edegem, Belgium
- Translational Neurosciences Research Group, University of Antwerp, Wilrijk, Belgium
| | - Marialuisa Zedde
- Neurology Unit, Stroke Unit, Azienda Unità Sanitaria Locale, IRCCS di Reggio Emilia, Reggio Emilia, Italy
| | - Johann Sellner
- Department of Neurology, Landesklinkum Mistelbach-Gänserndorf, Mistelbach, Austria
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17
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Tripathi P, Ansari MA, Gandhi TK, Albalwy F, Mehrotra R, Mishra D. Computational ensemble expert system classification for the recognition of bruxism using physiological signals. Heliyon 2024; 10:e25958. [PMID: 38390100 PMCID: PMC10881886 DOI: 10.1016/j.heliyon.2024.e25958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 01/31/2024] [Accepted: 02/05/2024] [Indexed: 02/24/2024] Open
Abstract
This study aimed to develop an automatic diagnostic scheme for bruxism, a sleep-related disorder characterized by teeth grinding and clenching. The aim was to improve on existing methods, which have been proven to be inefficient and challenging. We utilized a novel hybrid machine learning classifier, facilitated by the Weka tool, to diagnose bruxism from biological signals. The study processed and examined these biological signals by calculating the power spectral density. Data were categorized into normal or bruxism categories based on the EEG channel (C4-A1), and the sleeping phases were classified into wake (w) and rapid eye movement (REM) stages using the ECG channel (ECG1-ECG2). The classification resulted in a maximum specificity of 93% and an accuracy of 95% for the EEG-based diagnosis. The ECG-based classification yielded a supreme specificity of 87% and an accuracy of 96%. Furthermore, combining these phases using the EMG channel (EMG1-EMG2) achieved the highest specificity of 95% and accuracy of 98%. The ensemble Weka tool combined all three physiological signals EMG, ECG, and EEG, to classify the sleep stages and subjects. This integration increased the specificity and accuracy to 97% and 99%, respectively. This indicates that a more precise bruxism diagnosis can be obtained by including all three biological signals. The proposed method significantly improves bruxism diagnosis accuracy, potentially enhancing automatic home monitoring systems for this disorder. Future studies may expand this work by applying it to patients for practical use.
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Affiliation(s)
- Pragati Tripathi
- Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India
| | - M A Ansari
- Department of Electrical Engineering, Gautam Buddha University, Greater Noida, India
| | - Tapan Kumar Gandhi
- Department of Electrical Engineering, Indian Institute of Technology Delhi, India
| | - Faisal Albalwy
- Department of Computer Science, College of Computer Science and Engineering, Taibah University, Madinah, Saudi Arabia
- Division of Informatics, Imaging and Data Sciences, Stopford Building, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
| | - Rajat Mehrotra
- Department of Examination & Analysis, Amity University, Noida, India
| | - Deepak Mishra
- Department of Computer Science, College of Vocational Studies, University of Delhi, India
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18
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Ananthi N, Balaji V, Mohana M, Gnanapriya S. Smart plant disease net: Adaptive Dense Hybrid Convolution network with attention mechanism for IoT-based plant disease detection by improved optimization approach. Network 2024:1-39. [PMID: 38400837 DOI: 10.1080/0954898x.2024.2316080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/26/2024] [Indexed: 02/26/2024]
Abstract
Plant diseases are rising nowadays. Plant diseases lead to high economic losses. Internet of Things (IoT) technology has found its application in various sectors. This led to the introduction of smart farming, in which IoT has been utilized to help identify the exact spot of the diseased affected region on the leaf from the vast farmland in a well-organized and automated manner. Thus, the main focus of this task is the introduction of a novel plant disease detection model that relies on IoT technology. The collected images are given to the Image Transmission phase. Here, the encryption task is performed by employing the Advanced Encryption Standard (AES) and also the decrypted plant images are fed to the pre-processing stage. The Mask Regions with Convolutional Neural Networks (R-CNN) are used to segment the pre-processed images. Then, the segmented images are given to the detection phase in which the Adaptive Dense Hybrid Convolution Network with Attention Mechanism (ADHCN-AM) approach is utilized to perform the detection of plant disease. From the ADHCN-AM, the final detected plant disease outcomes are obtained. Throughout the entire validation, the offered model shows 95% enhancement in terms of MCC showcasing its effectiveness over the existing approaches.
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Affiliation(s)
- N Ananthi
- Department of Information Technology, Easwari Engineering College, Chennai, India
| | - V Balaji
- Department of CSE (Cyber Security), Easwari engineering college, Chennai, India
| | - M Mohana
- Department of Information Technology, Easwari Engineering College, Chennai, India
| | - S Gnanapriya
- Department of Information Technology, Easwari Engineering College, Chennai, India
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Jaiswal A, Washington P. Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study. JMIR Form Res 2024; 8:e52660. [PMID: 38354045 PMCID: PMC10902768 DOI: 10.2196/52660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/19/2023] [Accepted: 12/10/2023] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as "X") is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening. OBJECTIVE This study used Twitter as the primary source of data to study the behavioral characteristics and real-time emotional projections of individuals identifying with autism spectrum disorder (ASD). We aimed to improve the rigor of ASD analytics research by using the digital footprint of an individual to study the linguistic patterns of individuals with ASD. METHODS We developed a machine learning model to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users' self-identification with autism using "#ActuallyAutistic" and a separate control group to identify linguistic markers associated with ASD traits. To construct the data set, we targeted English-language tweets using the search query "#ActuallyAutistic" posted from January 1, 2014, to December 31, 2022. From these tweets, we identified unique users who used keywords such as "autism" OR "autistic" OR "neurodiverse" in their profile description and collected all the tweets from their timeline. To build the control group data set, we formulated a search query excluding the hashtag, "-#ActuallyAutistic," and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We also illustrate the utility of the data set through common natural language processing tasks such as sentiment analysis and topic modeling. RESULTS Our tweet classifier reached a 73% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. This is a promising start, demonstrating the potential for effective digital phenotyping studies and large-scale intervention using text data mined from social media. CONCLUSIONS Textual differences in social media communications can help researchers and clinicians conduct symptomatology studies in natural settings.
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Affiliation(s)
- Aditi Jaiswal
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
| | - Peter Washington
- Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States
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20
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Fu Y, Wei Y, Chen S, Chen C, Zhou R, Li H, Qiu M, Xie J, Huang D. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification. Phys Med Biol 2024; 69:045021. [PMID: 38271723 DOI: 10.1088/1361-6560/ad22a1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.
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Affiliation(s)
- Yong Fu
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Yuekun Wei
- School of Information and Management, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Siying Chen
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Caihong Chen
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Rong Zhou
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Hongjun Li
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Mochan Qiu
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Jin Xie
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Daizheng Huang
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
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21
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Revathi T, Balasubramaniam S, Sureshkumar V, Dhanasekaran S. An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction. Diagnostics (Basel) 2024; 14:239. [PMID: 38337755 PMCID: PMC10855367 DOI: 10.3390/diagnostics14030239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 01/17/2024] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.
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Affiliation(s)
- T.K. Revathi
- Department of Computer Science and Engineering, Sona College of Technology, Salem 636005, India;
| | | | - Vidhushavarshini Sureshkumar
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Vadapalani Campus, Chennai 600026, India;
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22
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Zhao L, Zhang Z. A improved pooling method for convolutional neural networks. Sci Rep 2024; 14:1589. [PMID: 38238357 PMCID: PMC10796389 DOI: 10.1038/s41598-024-51258-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
The pooling layer in convolutional neural networks plays a crucial role in reducing spatial dimensions, and improving computational efficiency. However, standard pooling operations such as max pooling or average pooling are not suitable for all applications and data types. Therefore, developing custom pooling layers that can adaptively learn and extract relevant features from specific datasets is of great significance. In this paper, we propose a novel approach to design and implement customizable pooling layers to enhance feature extraction capabilities in CNNs. The proposed T-Max-Avg pooling layer incorporates a threshold parameter T, which selects the K highest interacting pixels as specified, allowing it to control whether the output features of the input data are based on the maximum values or weighted averages. By learning the optimal pooling strategy during training, our custom pooling layer can effectively capture and represent discriminative information in the input data, thereby improving classification performance. Experimental results show that the proposed T-Max-Avg pooling layer achieves good performance on three different datasets. When compared to LeNet-5 model with average pooling, max pooling, and Avg-TopK methods, the T-Max-Avg pooling method achieves the highest accuracy on CIFAR-10, CIFAR-100, and MNIST datasets.
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Affiliation(s)
- Lei Zhao
- School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
| | - Zhonglin Zhang
- School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China.
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23
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Loomis JJ, de Araújo E Souza F, Angel M, Fabbri A. Technology-enhanced community forest management in tropical regions: A state of the art. J Environ Manage 2024; 350:119651. [PMID: 38039704 DOI: 10.1016/j.jenvman.2023.119651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/01/2023] [Accepted: 11/16/2023] [Indexed: 12/03/2023]
Abstract
Tropical forests provide ecosystem services to around 2.7 billion people. Yet they are reaching tipping points due to social, economic, and environmental pressures. Technology is increasingly being leveraged to expand Community Forest Management (CFM) monitoring capabilities and to potentially increase its effectiveness, but a systematic accounting of this is lacking in the scientific literature. This study employed a mixed-methods approach combining a systematic literature review (SLR) with semi-structured interviews of technology-enhanced CFM (tech-CFM) case studies in tropical forests. From the SLR, evaluation criteria were identified and applied to 23 case studies that employed one or more novel technologies, 8 on the African continent, 9 in the Asia Pacific region, 5 in Latin America, and 1 in multiple regions. The results include classifying 22 monitoring technologies, with satellite remote sensing technology being the most common (17 case studies), followed by mobile devices (10 case studies), which are often integrated with geographic information system (8 case studies) analysis and data platforms. These technologies tend to be deployed in packages that augment each technology's capabilities, beyond their individual uses. Nonetheless, they are limited by poor internet coverage in remote regions, impeding the ability to develop real-time integrated monitoring systems. Tech-CFM shows potential for complementing and integrating with national monitoring system when adequate data collection protocols are in place. Practical social-cultural, technical, and project design recommendations are made for the integration of technology into CFM. Finally, a multi-criteria decision-making framework is developed from the literature-based evaluation criteria to assist practitioners in selecting appropriate technology suites.
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Affiliation(s)
- John James Loomis
- Universidade Positivo, Graduate Program in Environmental Management (PPGAMB), Monitoring and Modeling Research Group, Rua Professor Pedro Viriato Parigot de Souza, 5300, Curitiba, PR, Brazil; Universidade Positivo, Brazil; Massachusetts Institute of Technology Environmental Solutions Initiative, 292 Main Street (E38), Cambridge, MA, 02142, United States; Getulio Vargas Foundation São Paulo School of Business Administration (FGV EAESP), Avenida 9 de Julho, 2029, São Paulo, SP, Brazil.
| | | | - Marcela Angel
- Massachusetts Institute of Technology Environmental Solutions Initiative, 292 Main Street (E38), Cambridge, MA, 02142, United States
| | - Alessandra Fabbri
- Massachusetts Institute of Technology Environmental Solutions Initiative, 292 Main Street (E38), Cambridge, MA, 02142, United States
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24
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Li L, Chen X, Hu S. Application of an end-to-end model with self-attention mechanism in cardiac disease prediction. Front Physiol 2024; 14:1308774. [PMID: 38283283 PMCID: PMC10811162 DOI: 10.3389/fphys.2023.1308774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/22/2023] [Indexed: 01/30/2024] Open
Abstract
Introduction: Heart disease is a prevalent global health challenge, necessitating early detection for improved patient outcomes. This study aims to develop an innovative heart disease prediction method using end-to-end deep learning, integrating self-attention mechanisms and generative adversarial networks to enhance predictive accuracy and efficiency in healthcare. Methods: We constructed an end-to-end model capable of processing diverse cardiac health data, including electrocardiograms, clinical data, and medical images. Self-attention mechanisms were incorporated to capture data correlations and dependencies, improving the extraction of latent features. Additionally, generative adversarial networks were employed to synthesize supplementary cardiac health data, augmenting the training dataset. Experiments were conducted using publicly available heart disease datasets for training, validation, and testing. Multiple evaluation metrics, including accuracy, recall, and F1-score, were employed to assess model performance. Results: Our model consistently outperformed traditional methods, achieving accuracy rates exceeding 95% on multiple datasets. Notably, the recall metric demonstrated the model's effectiveness in identifying heart disease patients, with rates exceeding 90%. The comprehensive F1-score also indicated exceptional performance, achieving optimal results. Discussion: This research highlights the potential of end-to-end deep learning with self-attention mechanisms in heart disease prediction. The model's consistent success across diverse datasets offers new possibilities for early diagnosis and intervention, ultimately enhancing patients' quality of life and health. These findings hold significant clinical application prospects and promise substantial advancements in the healthcare field.
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Affiliation(s)
- Li Li
- Medical and Health College, Xuchang Vocational Technical College, Xuchang, China
| | - Xi Chen
- Public Education Department, Xuchang Vocational Technical College, Xuchang, China
| | - Sanjun Hu
- Xuchang Vocational and Technical College, School of Information Engineering, Xuchang, China
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25
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Wang K, Cao S, Kaur J, Ghafurian M, Butt ZA, Morita P. Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults. Front Artif Intell 2024; 6:1342427. [PMID: 38282903 PMCID: PMC10811001 DOI: 10.3389/frai.2023.1342427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 12/29/2023] [Indexed: 01/30/2024] Open
Abstract
Background As global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults. Methods The study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron. Results All machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals' daily living activities and their corresponding heart rate responses, with the highest R2 value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance. Conclusions The utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals' daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual's cardiovascular health profile. This valuable information can inform the implementation of personalized interventions, preventive measures, and lifestyle modifications to mitigate the risk of cardiovascular diseases and improve overall health outcomes.
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Affiliation(s)
- Kang Wang
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Shi Cao
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Jasleen Kaur
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Moojan Ghafurian
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Plinio Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
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26
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Karimi Darvanjooghi MH, Magdouli S, Brar SK. Recent challenges in biological cyanidation and oxidation of sulfide-based refractory gold ore. World J Microbiol Biotechnol 2024; 40:67. [PMID: 38197973 DOI: 10.1007/s11274-024-03887-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/07/2024] [Indexed: 01/11/2024]
Abstract
In mining industries, biomining (comprising biooxidation and bioleaching) is implemented to extract metals from specific ores and waste streams with less environmental effect and expense. Usually, micron-sized gold particles are held in a crystal lattice of iron sulfide minerals and expensively extracted using common approaches. Researchers and industries are interested in developing recent technology and biologically sustainable methods in both pretreatment and further extraction steps for extracting this valuable metal from ores. Diverse studies in biooxidation, as a conventional pretreatment, and biocyanidation, as a new proposed biotechnological method in the downstream gold extraction step, have addressed scientific and technological issues in the extraction of this metal. These two methods have become economically practical by merging high-throughput microbiological data, extraction and recovery process knowledge, and theory validation. However, there is still a gap in the implementation of both the pretreatment method and extraction method due to the consistency and their compatibility with operational recovery conditions. This review brings out the recent biooxidation and biocyanidation improvements, innovation, industry and academic research, and obstacles to gold extraction with a brief explanation to address the recent developments.
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Affiliation(s)
| | - Sara Magdouli
- Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada
| | - Satinder Kaur Brar
- Department of Civil Engineering, Lassonde School of Engineering, York University, Toronto, ON, M3J 1P3, Canada.
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27
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Ogunpola A, Saeed F, Basurra S, Albarrak AM, Qasem SN. Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics (Basel) 2024; 14:144. [PMID: 38248021 PMCID: PMC10813849 DOI: 10.3390/diagnostics14020144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 12/21/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024] Open
Abstract
Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study's primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study's outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model's diagnostic accuracy for heart disease.
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Affiliation(s)
- Adedayo Ogunpola
- DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; (A.O.); (S.B.)
| | - Faisal Saeed
- DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; (A.O.); (S.B.)
| | - Shadi Basurra
- DAAI Research Group, College of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK; (A.O.); (S.B.)
| | - Abdullah M. Albarrak
- Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.M.A.); (S.N.Q.)
| | - Sultan Noman Qasem
- Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (A.M.A.); (S.N.Q.)
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28
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Rahman AU, Alsenani Y, Zafar A, Ullah K, Rabie K, Shongwe T. Enhancing heart disease prediction using a self-attention-based transformer model. Sci Rep 2024; 14:514. [PMID: 38177293 PMCID: PMC10767116 DOI: 10.1038/s41598-024-51184-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 01/01/2024] [Indexed: 01/06/2024] Open
Abstract
Cardiovascular diseases (CVDs) continue to be the leading cause of more than 17 million mortalities worldwide. The early detection of heart failure with high accuracy is crucial for clinical trials and therapy. Patients will be categorized into various types of heart disease based on characteristics like blood pressure, cholesterol levels, heart rate, and other characteristics. With the use of an automatic system, we can provide early diagnoses for those who are prone to heart failure by analyzing their characteristics. In this work, we deploy a novel self-attention-based transformer model, that combines self-attention mechanisms and transformer networks to predict CVD risk. The self-attention layers capture contextual information and generate representations that effectively model complex patterns in the data. Self-attention mechanisms provide interpretability by giving each component of the input sequence a certain amount of attention weight. This includes adjusting the input and output layers, incorporating more layers, and modifying the attention processes to collect relevant information. This also makes it possible for physicians to comprehend which features of the data contributed to the model's predictions. The proposed model is tested on the Cleveland dataset, a benchmark dataset of the University of California Irvine (UCI) machine learning (ML) repository. Comparing the proposed model to several baseline approaches, we achieved the highest accuracy of 96.51%. Furthermore, the outcomes of our experiments demonstrate that the prediction rate of our model is higher than that of other cutting-edge approaches used for heart disease prediction.
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Affiliation(s)
- Atta Ur Rahman
- Riphah Institute of System Engineering, Riphah International University Islamabad, Islamabad, 46000, Pakistan.
- Research and Development Department, Lun Startup Studio, 11543, Riyadh, Saudi Arabia.
| | - Yousef Alsenani
- Department of Information Systems, FCIT, King Abdulaziz University, 21443, Jeddah, Saudi Arabia
- Research and Development Department, Lun Startup Studio, 11543, Riyadh, Saudi Arabia
| | - Adeel Zafar
- Riphah Institute of System Engineering, Riphah International University Islamabad, Islamabad, 46000, Pakistan
| | - Kalim Ullah
- Department of Zoology, Kohat University of Science and Technology, Kohat, 26000, Pakistan
| | - Khaled Rabie
- Department of Engineering, Manchester Metropolitan University, Manchester, M15 6BH, UK
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa
| | - Thokozani Shongwe
- Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg, 2006, South Africa
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29
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Sornalakshmi M, Devakanth JJMA, Rajalakshmi R, Velmurugadass P. An energy-aware heart disease prediction system using ESMO and optimal deep learning model for healthcare monitoring in IoT. J Biomol Struct Dyn 2024:1-15. [PMID: 38165748 DOI: 10.1080/07391102.2023.2298736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 12/18/2023] [Indexed: 01/04/2024]
Abstract
The Internet of Things (IoT), which provides seamless connectivity between people and things, improves our quality of life. In the medical field, predictive analytics can help transform a reactive healthcare (HC) strategy into a proactive one. The HC industry embraces cutting-edge artificial intelligence and machine learning (ML) technologies. ML's area of deep learning has the revolutionary potential to reliably analyze massive volumes of data quickly, produce insightful revelations and solve challenging issues. This article proposes an energy-aware heart disease prediction (HDP) system based on enhanced spider monkey optimization (ESMO) and a weight-optimized neural network for an IoT-based HC environment. The proposed work consists of two essential phases: energy-efficient data transmission and HDP. In energy-efficient transmission, the cluster leaders are optimally selected using ESMO and the cluster formation is done based on Euclidean distance. In HDP, the patient data are collected from the dataset, and essential features are extracted. After that, the dimensionality reduction is carried out using the modified linear discriminant analysis approach to reduce over-fitting issues. Finally, the HDP uses the enhanced Archimedes weight-optimized deep neural network (EAWO-DNN). The simulation findings demonstrate that the proposed optimal clustering mechanism enhances the network's lifespan by consuming minimal energy compared to the existing techniques. Also, the proposed EAWO-DNN classifier achieves higher prediction accuracy, precision, recall and f-measure than the conventional methods for predicting heart disease in IoT.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- M Sornalakshmi
- PG Department of Computer Science, Arulmigu Kalasalingam College of Arts and Science, Krishnan Koil, Tamil Nadu, India
| | - J Jude Moses Anto Devakanth
- Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
| | - R Rajalakshmi
- Department of Electronics and Communication Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India
| | - P Velmurugadass
- Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnan Koil, Tamil Nadu, India
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Bertl M, Bignoumba N, Ross P, Yahia SB, Draheim D. Evaluation of deep learning-based depression detection using medical claims data. Artif Intell Med 2024; 147:102745. [PMID: 38184352 DOI: 10.1016/j.artmed.2023.102745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 01/08/2024]
Abstract
Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often not evaluated based on large, real-world data. This research shows the potential of using deep learning on the medical claims data of 812,853 people between 2018 and 2022, with 26,973,943 ICD-10-coded diseases, to predict depression (F32 and F33 ICD-10 codes). The dataset used represents almost the entire adult population of Estonia. Based on these data, to show the critical importance of the underlying temporal properties of the data for the detection of depression, we evaluate the performance of non-sequential models (LR, FNN), sequential models (LSTM, CNN-LSTM) and the sequential model with a decay factor (GRU-Δt, GRU-decay). Furthermore, since explainability is necessary for the medical domain, we combine a self-attention model with the GRU decay and evaluate its performance. We named this combination Att-GRU-decay. After extensive empirical experimentation, our model (Att-GRU-decay), with an AUC score of 0.990, an AUPRC score of 0.974, a specificity of 0.999 and a sensitivity of 0.944, proved to be the most accurate. The results of our novel Att-GRU-decay model outperform the current state of the art, demonstrating the potential usefulness of deep learning algorithms for DDSS development. We further expand this by describing a possible application scenario of the proposed algorithm for depression screening in a general practitioner (GP) setting-not only to decrease healthcare costs, but also to improve the quality of care and ultimately decrease people's suffering.
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Affiliation(s)
- Markus Bertl
- Department of Health Technologies, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia.
| | - Nzamba Bignoumba
- Department of Software Science, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia
| | - Peeter Ross
- Department of Health Technologies, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia; Department of Research, East-Tallinn Central Hospital, Ravi 18, Tallinn, 10138, Estonia
| | - Sadok Ben Yahia
- Department of Software Science, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia; University of Southern Denmark, Alsion 2, Sønderborg, 6400, Denmark
| | - Dirk Draheim
- Information Systems Group, Tallinn University of Technology, Akadeemia Tee 15A, Tallinn, 12618, Estonia
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31
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Sutradhar A, Al Rafi M, Shamrat FMJM, Ghosh P, Das S, Islam MA, Ahmed K, Zhou X, Azad AKM, Alyami SA, Moni MA. BOO-ST and CBCEC: two novel hybrid machine learning methods aim to reduce the mortality of heart failure patients. Sci Rep 2023; 13:22874. [PMID: 38129433 PMCID: PMC10739972 DOI: 10.1038/s41598-023-48486-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
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Affiliation(s)
- Ananda Sutradhar
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - Mustahsin Al Rafi
- Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City (DSC), Birulia, Savar, Dhaka, 1216, Bangladesh
| | - F M Javed Mehedi Shamrat
- Department of Computer System and Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia
| | - Pronab Ghosh
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Subrata Das
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Md Anaytul Islam
- Department of Computer Science, Lakehead University, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada
| | - Kawsar Ahmed
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK, S7N 5A9, Canada
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
- Health Informatics Research Lab, Department of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka, 1216, Bangladesh
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Toowoomba, Australia
| | - A K M Azad
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia
| | - Salem A Alyami
- Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), 13318, Riyadh, Saudi Arabia
| | - Mohammad Ali Moni
- Centre for AI & Digital Health Technology, Artificial Intelligence & Cyber Future Institute, Charles Stuart University, Bathurst, NSW, 2795, Australia.
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Panç K, Hürsoy N, Başaran M, Yazici MM, Kaba E, Nalbant E, Gündoğdu H, Gürün E. Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets. Cureus 2023; 15:e50932. [PMID: 38249212 PMCID: PMC10800012 DOI: 10.7759/cureus.50932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2023] [Indexed: 01/23/2024] Open
Abstract
Background The COVID-19 infection has spread rapidly since its emergence and has affected a large part of the global population. With the increasing number of cases, researchers are trying to predict the prognosis of patients by using different data with artificial intelligence methods such as machine learning (ML). In this study, we aimed to predict mortality risk in COVID-19 patients using ML algorithms with different datasets. Methodology In this retrospective study, we evaluated the fever, oxygen saturation, laboratory results, thorax computed tomography (CT) findings, and comorbid diseases at admission to the hospital of 404 patients whose diagnosis was confirmed by the reverse transcription polymerase chain reaction test. Different datasets were created by combining the data. The Synthetic Minority Oversampling Technique was used to reduce the imbalance in the dataset. K-nearest neighbors, support vector machine, stochastic gradient descent, random forest, neural network, naive Bayes, logistic regression, gradient boosting, XGBoost, and AdaBoost models were used to create the ML algorithm, and the accuracy rates of mortality prediction were compared. Results When the dataset was created with CT parenchyma score, pulmonary artery and inferior vena cava diameters, and laboratory results, mortality was predicted with an accuracy of 98.4% with the gradient boosting model. Conclusions The study demonstrates that patient prognosis can be accurately predicted using simple measurements from thorax CT scans and laboratory findings.
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Affiliation(s)
- Kemal Panç
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Nur Hürsoy
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Mustafa Başaran
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Mümin Murat Yazici
- Emergency Medicine, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Esat Kaba
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | | | - Hasan Gündoğdu
- Radiology, Recep Tayyip Erdoğan Education and Research Hospital, Rize, TUR
| | - Enes Gürün
- Radiology, Samsun University, Samsun, TUR
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Hooshmand MJ, Sakib-Uz-Zaman C, Khondoker MAH. Machine Learning Algorithms for Predicting Mechanical Stiffness of Lattice Structure-Based Polymer Foam. Materials (Basel) 2023; 16:7173. [PMID: 38005102 PMCID: PMC10672764 DOI: 10.3390/ma16227173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/03/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023]
Abstract
Polymer foams are extensively utilized because of their superior mechanical and energy-absorbing capabilities; however, foam materials of consistent geometry are difficult to produce because of their random microstructure and stochastic nature. Alternatively, lattice structures provide greater design freedom to achieve desired material properties by replicating mesoscale unit cells. Such complex lattice structures can only be manufactured effectively by additive manufacturing or 3D printing. The mechanical properties of lattice parts are greatly influenced by the lattice parameters that define the lattice geometries. To study the effect of lattice parameters on the mechanical stiffness of lattice parts, 360 lattice parts were designed by varying five lattice parameters, namely, lattice type, cell length along the X, Y, and Z axes, and cell wall thickness. Computational analyses were performed by applying the same loading condition on these lattice parts and recording corresponding strain deformations. To effectively capture the correlation between these lattice parameters and parts' stiffness, five machine learning (ML) algorithms were compared. These are Linear Regression (LR), Polynomial Regression (PR), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). Using evaluation metrics such as mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE), all ML algorithms exhibited significantly low prediction errors during the training and testing phases; however, the Taylor diagram demonstrated that ANN surpassed other algorithms, with a correlation coefficient of 0.93. That finding was further supported by the relative error box plot and by comparing actual vs. predicted values plots. This study revealed the accurate prediction of the mechanical stiffness of lattice parts for the desired set of lattice parameters.
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Affiliation(s)
| | | | - Mohammad Abu Hasan Khondoker
- Industrial Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada; (M.J.H.); (C.S.-U.-Z.)
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Ahmad IS, Li N, Wang T, Liu X, Dai J, Chan Y, Liu H, Zhu J, Kong W, Lu Z, Xie Y, Liang X. COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning. Bioengineering (Basel) 2023; 10:1314. [PMID: 38002438 PMCID: PMC10669345 DOI: 10.3390/bioengineering10111314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 10/28/2023] [Accepted: 11/02/2023] [Indexed: 11/26/2023] Open
Abstract
The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical. Ultra-low-dose X-ray imaging technology enables rapid and accurate COVID-19 detection with minimal additional radiation exposure. In this retrospective cohort study, ULTRA-X-COVID, a deep neural network specifically designed for automatic detection of COVID-19 infections using ultra-low-dose X-ray images, is presented. The study included a multinational and multicenter dataset consisting of 30,882 X-ray images obtained from approximately 16,600 patients across 51 countries. It is important to note that there was no overlap between the training and test sets. The data analysis was conducted from 1 April 2020 to 1 January 2022. To evaluate the effectiveness of the model, various metrics such as the area under the receiver operating characteristic curve, receiver operating characteristic, accuracy, specificity, and F1 score were utilized. In the test set, the model demonstrated an AUC of 0.968 (95% CI, 0.956-0.983), accuracy of 94.3%, specificity of 88.9%, and F1 score of 99.0%. Notably, the ULTRA-X-COVID model demonstrated a performance comparable to conventional X-ray doses, with a prediction time of only 0.1 s per image. These findings suggest that the ULTRA-X-COVID model can effectively identify COVID-19 cases using ultra-low-dose X-ray scans, providing a novel alternative for COVID-19 detection. Moreover, the model exhibits potential adaptability for diagnoses of various other diseases.
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Affiliation(s)
- Isah Salim Ahmad
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Na Li
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Tangsheng Wang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Xuan Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Jingjing Dai
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Yinping Chan
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Haoyang Liu
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Junming Zhu
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Weibin Kong
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Zefeng Lu
- Department of Biomedical Engineering, Guangdong Medical University, Dongguan 523808, China; (N.L.); (H.L.); (J.Z.); (W.K.); (Z.L.)
| | - Yaoqin Xie
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
| | - Xiaokun Liang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; (I.S.A.); (T.W.); (X.L.); (J.D.); (Y.C.); (Y.X.)
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Sánchez-Recio R, Samper-Pardo M, Llopis-Lambán R, Oliván-Blázquez B, Cerdan-Bernad M, Magallón-Botaya R. Self-rated health impact of COVID 19 confinement on inmates in Southeastern of Europe: a qualitative study. BMC Public Health 2023; 23:2183. [PMID: 37936162 PMCID: PMC10631134 DOI: 10.1186/s12889-023-17088-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 10/27/2023] [Indexed: 11/09/2023] Open
Abstract
INTRODUCTION The COVID-19 pandemic necessitated the implementation of various measures within closed institutions like prisons to control the spread of the virus. Analyzing the impact of these measures on the health of inmates is crucial from a public health perspective. This study aimed to explore inmates' subjective perception of the COVID-19 lockdown, the implemented measures, their physical self-perception, and their views on the vaccination process. METHOD Between April 2021 and January 2022, 27 semi-structured individual interviews and 1 focus group were conducted with inmates in a prison located in northwest Spain. The interviews were conducted in person and audio-recorded. Thematic content analysis was employed, utilizing methodological triangulation to enhance the coherence and rigor of the results. RESULTS The analysis revealed two main themes and nine subthemes. The first theme focused on inmates' perception of the implementation of protective measures against COVID-19 within the prison and its impact on their well-being. The second theme explored the pandemic's emotional impact on inmates. All participants reported negative consequences on their health resulting from the measures implemented by the institution to contain the pandemic. However, they acknowledged that measures like lockdowns and mass vaccination helped mitigate the spread of the virus within the prison, contrary to initial expectations. CONCLUSION COVID-19 and related measures have directly affected the health of inmates. To improve their health and minimize the impact of pandemic-induced changes, community participation and empowerment of individuals are essential tools, particularly within closed institutions such as prisons.
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Affiliation(s)
- Raquel Sánchez-Recio
- Research Group on Health Services in Aragon (GRISSA), Department of Preventive Medicine and Public Health, Faculty of Social and Labor Sciences, University of Zaragoza, C/ Violante de Hungría (23), Zaragoza, 50009, Spain
- Institute for Health Research in Aragon (IIS Aragón), C. de San Juan Bosco, 13, Zaragoza, 50009, Spain
- Zaragoza Penitentiary Center, Autovía A-23, Km, 328, Zaragoza, Spain
| | - Mario Samper-Pardo
- Department of medicine, Facultad de Medicina Edificio A, University of Zaragoza, Zaragoza, 5009, Spain
| | | | - Bárbara Oliván-Blázquez
- Institute for Health Research in Aragon (IIS Aragón), C. de San Juan Bosco, 13, Zaragoza, 50009, Spain.
- Department of Psychology and Sociology, University of Zaragoza, Calle de Violante de Hungría, 23, Zaragoza, 2009, Spain.
| | | | - Rosa Magallón-Botaya
- Institute for Health Research in Aragon (IIS Aragón), C. de San Juan Bosco, 13, Zaragoza, 50009, Spain
- Department of medicine, Facultad de Medicina Edificio A, University of Zaragoza, Zaragoza, 5009, Spain
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Umer M, Aljrees T, Karamti H, Ishaq A, Alsubai S, Omar M, Bashir AK, Ashraf I. Heart failure patients monitoring using IoT-based remote monitoring system. Sci Rep 2023; 13:19213. [PMID: 37932424 PMCID: PMC10628138 DOI: 10.1038/s41598-023-46322-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/30/2023] [Indexed: 11/08/2023] Open
Abstract
Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy.
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Affiliation(s)
- Muhammad Umer
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Turki Aljrees
- Department College of Computer Science and Engineering, University of Hafr Al-Batin, 39524, Hafar Al-Batin, Saudi Arabia
| | - Hanen Karamti
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, 11671, Riyadh, Saudi Arabia
| | - Abid Ishaq
- Department of Computer Science and Information Technology, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, 11942, Al-Kharj, Saudi Arabia
| | - Marwan Omar
- Information Technology and Management, Illinois Institute of Technology, Chicago, USA
| | - Ali Kashif Bashir
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK.
- Woxsen School of Business, Woxsen University, Hyderabad, 502 345, India.
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
| | - Imran Ashraf
- Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea.
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Khullar V, Singh HP. Vocal-friend: internet of social-things framework to aid verbal communication. Disabil Rehabil Assist Technol 2023; 18:1527-1535. [PMID: 35404708 DOI: 10.1080/17483107.2022.2060349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 03/26/2022] [Indexed: 10/18/2022]
Abstract
PURPOSE Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. MATERIAL AND METHODS Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. RESULTS The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. CONCLUSION In terms of technical implementations and satisfaction analysis, results found acceptable with above average score.IMPLICATION FOR REHABILITATIONProposed framework is easy to use by caregivers with even having little knowledge.Support individual with deficit to learn social verbal communication skill to survive in society.Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits.Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc.
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Affiliation(s)
- Vikas Khullar
- Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India
| | - Harjit Pal Singh
- CT Institute of Engineering, Management and Technology, Punjab, India
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Zheng D, Tang P, Lu D, Han L, Saberi S. A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis. J Cancer Res Clin Oncol 2023; 149:14519-14534. [PMID: 37567985 DOI: 10.1007/s00432-023-05238-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023]
Abstract
INTRODUCTION Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants. BACKGROUND Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision. METHODOLOGY This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier. RESULTS The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features. CONCLUSION Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early.
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Affiliation(s)
- Dengru Zheng
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China.
| | - Ping Tang
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China
| | - Danping Lu
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China
| | - Liangfu Han
- Cancer Center, Foshan Fuxing Chancheng Hospital, Foshan, 528000, Guangdong, China
| | - Sajjad Saberi
- Department of Computer Science, Khayyam University, Mashhad, Iran.
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Ba F, Peng P, Zhang Y, Zhao Y. Classification and Identification of Contaminants in Recyclable Containers Based on a Recursive Feature Elimination-Light Gradient Boosting Machine Algorithm Using an Electronic Nose. Micromachines (Basel) 2023; 14:2047. [PMID: 38004904 PMCID: PMC10673532 DOI: 10.3390/mi14112047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023]
Abstract
Establishing an excellent recycling mechanism for containers is of great importance for environmental protection, so many technical approaches applied during the whole recycling stage have become popular research issues. Among them, classification is considered a key step, but this work is mostly achieved manually in practical applications. Due to the influence of human subjectivity, the classification accuracy often varies significantly. In order to overcome this shortcoming, this paper proposes an identification method based on a Recursive Feature Elimination-Light Gradient Boosting Machine (RFE-LightGBM) algorithm using electronic nose. Firstly, odor features were extracted, and feature datasets were then constructed based on the response data of the electronic nose to the detected gases. Afterwards, a principal component analysis (PCA) and the RFE-LightGBM algorithm were applied to reduce the dimensionality of the feature datasets, and the differences between these two methods were analyzed, respectively. Finally, the differences in the classification accuracies on the three datasets (the original feature dataset, PCA dimensionality reduction dataset, and RFE-LightGBM dimensionality reduction dataset) were discussed. The results showed that the highest classification accuracy of 95% could be obtained by using the RFE-LightGBM algorithm in the classification stage of recyclable containers, compared to the original feature dataset (88.38%) and PCA dimensionality reduction dataset (92.02%).
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Affiliation(s)
| | | | | | - Yongli Zhao
- School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
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Musa HS, Krichen M, Altun AA, Ammi M. Survey on Blockchain-Based Data Storage Security for Android Mobile Applications. Sensors (Basel) 2023; 23:8749. [PMID: 37960449 PMCID: PMC10650731 DOI: 10.3390/s23218749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023]
Abstract
This research paper investigates the integration of blockchain technology to enhance the security of Android mobile app data storage. Blockchain holds the potential to significantly improve data security and reliability, yet faces notable challenges such as scalability, performance, cost, and complexity. In this study, we begin by providing a thorough review of prior research and identifying critical research gaps in the field. Android's dominant position in the mobile market justifies our focus on this platform. Additionally, we delve into the historical evolution of blockchain and its relevance to modern mobile app security in a dedicated section. Our examination of encryption techniques and the effectiveness of blockchain in securing mobile app data storage yields important insights. We discuss the advantages of blockchain over traditional encryption methods and their practical implications. The central contribution of this paper is the Blockchain-based Secure Android Data Storage (BSADS) framework, now consisting of six comprehensive layers. We address challenges related to data storage costs, scalability, performance, and mobile-specific constraints, proposing technical optimization strategies to overcome these obstacles effectively. To maintain transparency and provide a holistic perspective, we acknowledge the limitations of our study. Furthermore, we outline future directions, stressing the importance of leveraging lightweight nodes, tackling scalability issues, integrating emerging technologies, and enhancing user experiences while adhering to regulatory requirements.
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Affiliation(s)
- Hussam Saeed Musa
- Faculty of Technology, Department of Computer Engineering, Selçuk University, 42130 Konya, Turkey; (H.S.M.); (A.A.A.)
| | - Moez Krichen
- Faculty of Computer Science and Information Technology, Al-Baha University, Al Baha 65431, Saudi Arabia
- ReDCAD Laboratory, National School of Engineers of Sfax, University of Sfax, Sfax 3000, Tunisia
| | - Adem Alpaslan Altun
- Faculty of Technology, Department of Computer Engineering, Selçuk University, 42130 Konya, Turkey; (H.S.M.); (A.A.A.)
| | - Meryem Ammi
- Digital Forensics Department, Criminal Justice College, Naif Arab University for Security Sciences, Riyadh 14812, Saudi Arabia;
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Rahman MM, Nasir MK, Nur-A-Alam M, Khan MSI. Proposing a hybrid technique of feature fusion and convolutional neural network for melanoma skin cancer detection. J Pathol Inform 2023; 14:100341. [PMID: 38028129 PMCID: PMC10630642 DOI: 10.1016/j.jpi.2023.100341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/20/2023] [Accepted: 10/09/2023] [Indexed: 12/01/2023] Open
Abstract
Skin cancer is among the most common cancer types worldwide. Automatic identification of skin cancer is complicated because of the poor contrast and apparent resemblance between skin and lesions. The rate of human death can be significantly reduced if melanoma skin cancer could be detected quickly using dermoscopy images. This research uses an anisotropic diffusion filtering method on dermoscopy images to remove multiplicative speckle noise. To do this, the fast-bounding box (FBB) method is applied here to segment the skin cancer region. We also employ 2 feature extractors to represent images. The first one is the Hybrid Feature Extractor (HFE), and second one is the convolutional neural network VGG19-based CNN. The HFE combines 3 feature extraction approaches namely, Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF) into a single fused feature vector. The CNN method is also used to extract additional features from test and training datasets. This 2-feature vector is then fused to design the classification model. The proposed method is then employed on 2 datasets namely, ISIC 2017 and the academic torrents dataset. Our proposed method achieves 99.85%, 91.65%, and 95.70% in terms of accuracy, sensitivity, and specificity, respectively, making it more successful than previously proposed machine learning algorithms.
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Affiliation(s)
- Md. Mahbubur Rahman
- Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Mostofa Kamal Nasir
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Md. Nur-A-Alam
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of CSE, Dhaka International University, Dhaka 1205, Bangladesh
| | - Md. Saikat Islam Khan
- Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of CSE, Dhaka International University, Dhaka 1205, Bangladesh
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R. S, P. K. Heart disease severity level identification system on Hyperledger consortium network. PeerJ Comput Sci 2023; 9:e1626. [PMID: 37869454 PMCID: PMC10588697 DOI: 10.7717/peerj-cs.1626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/08/2023] [Indexed: 10/24/2023]
Abstract
Electronic Health Records (EHRs) play a vital role in the healthcare domain for the patient survival system. They can include detailed information such as medical histories, medications, allergies, immunizations, vital signs, and more. It can help to reduce medical errors, improve patient safety, and increase efficiency in healthcare delivery. EHR approaches are proven to be an efficient and successful way of sharing patients' personal health information. These kinds of highly sensitive information are vulnerable to privacy and security associated threats. As a result, new solutions must develop to meet the privacy and security concerns in health information systems. Blockchain technology has the potential to revolutionize the way electronic health records (EHRs) are stored, accessed, and utilized by healthcare providers. By utilizing a distributed ledger, blockchain technology can help ensure that data is immutable and secure from tampering. In this article, a Hyperledger consortium network has been developed for sharing health records with enhanced privacy and security. The attribute based access control (ABAC) mechanism is used for controlling access to electronic health records. The use of ABAC on the network provides EHRs with an extra layer of security and control, ensuring that only authorized users have access to sensitive data. By using attributes such as user identity, role, and health condition, it is possible to precisely control access to records on blockchain. Besides, a Gaussian naïve Bayes algorithm has been integrated with this consortium network for prediction of cardiovascular disease. The prediction of cardiovascular is difficult due to its correlated risk factors. This system is beneficial for both patients and physicians as it allows physicians to quickly identify high-risk patients and easily provide them with patient severity level using feature weight prediction algorithms. Dynamic emergency access control privileges are used for the emergency team and will be withdrawn once the emergency has been resolved, depending on the severity score. The system is implemented with the following medical datasets: the heart disease dataset, the Pima Indian diabetes dataset, the stroke prediction dataset, and the body fat prediction dataset. The above datasets are obtained from the Kaggle repository. This system evaluates system performance by simulating various operations using the Hyperledger Caliper benchmarking tool. The performance metrics such as latency, transaction rate, resource utilization, etc. are measured and compared with the benchmark.
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Affiliation(s)
- Sasikumar R.
- Computer Science and Engineering, K.Ramakrishnan College of Engineering, Tiruchirappalli, Tamilnadu, India
| | - Karthikeyan P.
- Information Technology, Thiagarajar College of Engineering, Madurai, Tamilnadu, India
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Kaur G, Garg M, Gupta S, Juneja S, Rashid J, Gupta D, Shah A, Shaikh A. Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model. Diagnostics (Basel) 2023; 13:3152. [PMID: 37835895 PMCID: PMC10572820 DOI: 10.3390/diagnostics13193152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 09/23/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.
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Affiliation(s)
- Gurjinder Kaur
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Meenu Garg
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sheifali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Sapna Juneja
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Junaid Rashid
- Department of Data Science, Sejong University, Seoul 05006, Republic of Korea;
| | - Deepali Gupta
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India; (G.K.); (M.G.); (S.G.); (D.G.)
| | - Asadullah Shah
- Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, Kuala Lumpur 53100, Malaysia;
| | - Asadullah Shaikh
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia;
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Rout SK, Meher A, Behera P, de Broucker G, Kadam SM. How a low income state of India managed the unemployment situation during COVID-19? Lessons for future pandemic management. J Public Health Res 2023; 12:22799036231208425. [PMID: 38034847 PMCID: PMC10683399 DOI: 10.1177/22799036231208425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 08/28/2023] [Indexed: 12/02/2023] Open
Abstract
Background The partial and complete lockdown to curb the spread of COVID-19 caused enormous economic and social disruptions throughout the world. India witnessed the sharpest decline in its Gross Domestic Product (GDP), and the unemployment rate rose sharply in the first quarter of 2020-21. Odisha, one of the low income states of India, has faced a steep rise in unemployment, with lakhs of migrant workers returning to the state. This article attempts to examine Odisha's unemployment situation compared to the low-income states of India as well as with the national average during COVID-19. This also investigates to what extent the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA) provided relief to the people by providing short-term employment opportunities. Design This is a descriptive study and is based upon repetitive cross sectional secondary data on unemployment rate and labour force participation rate across the low-income states of India. Method The study used descriptive statistics to analyze the secondary data from the Center for Monitoring Indian Economy (CMIE) and MGNREGA report. The labour force participation rate (LFPR) and unemployment rate (UER) data were collected from the CMIE trimester reports. The information related to number days of employment demanded and employment provided were collected from the MGNREGA reports. Total time period was divided in to two parts - 2017-19 pre pandemic period and 2020-2021 pandemic period. Results The analysis of UER revealed that the unemployment situation in Odisha was better than the low-income states and overall India. The UER during COVID-19 (Sep-Dec 2020 to Sep-Dec 2021) was lower than the pre COVID-19 level in Odisha (1.6% in Sep-Dec 2020), compared to all India, where this was more than the pre-COVID-19 level (7.4% in Sep-Dec 2020). Odisha government had nearly doubled the employment generation through MGNREGA during 2020-21.The state government undertook a number of proactive measures - increasing wage rate, providing extra days of work in vulnerable districts to address the unemployment situation during the pandemic. Conclusion The state government's effort to manage the livelihood crisis was notable during the pandemic.. Proper implementation of the wage employment programmes led to higher decline in the UER in Odisha compared to other states These experiences can be emulated by other states or countries.
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Affiliation(s)
| | - Ananda Meher
- Kalinga Institute of Industrial Technology (KIIT) University, Odisha, India
| | - Pallavi Behera
- Indian Institute of Public Health, Bhubaneswar, Odisha, India
| | - Gatien de Broucker
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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Verma P, Gupta A, Kumar M, Gill SS. FCMCPS-COVID: AI propelled fog-cloud inspired scalable medical cyber-physical system, specific to coronavirus disease. Internet Things (Amst) 2023; 23:100828. [PMID: 37274449 PMCID: PMC10214767 DOI: 10.1016/j.iot.2023.100828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/11/2023] [Accepted: 05/20/2023] [Indexed: 06/06/2023]
Abstract
Medical cyber-physical systems (MCPS) firmly integrate a network of medical objects. These systems are highly efficacious and have been progressively used in the Healthcare 4.0 to achieve continuous high-quality services. Healthcare 4.0 encompasses numerous emerging technologies and their applications have been realized in the monitoring of a variety of virus outbreaks. As a growing healthcare trend, coronavirus disease (COVID-19) can be cured and its spread can be prevented using MCPS. This virus spreads from human to human and can have devastating consequences. Moreover, with the alarmingly rising death rate and new cases across the world, there is an urgent need for continuous identification and screening of infected patients to mitigate their spread. Motivated by the facts, we propose a framework for early detection, prevention, and control of the COVID-19 outbreak by using novel Industry 5.0 technologies. The proposed framework uses a dimensionality reduction technique in the fog layer, allowing high-quality data to be used for classification purposes. The fog layer also uses the ensemble learning-based data classification technique for the detection of COVID-19 patients based on the symptomatic dataset. In addition, in the cloud layer, social network analysis (SNA) has been performed to control the spread of COVID-19. The experimental results reveal that compared with state-of-the-art methods, the proposed framework achieves better results in terms of accuracy (82.28 %), specificity (91.42 %), sensitivity (90 %) and stability with effective response time. Furthermore, the utilization of CVI-based alert generation at the fog layer improves the novelty aspects of the proposed system.
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Affiliation(s)
- Prabal Verma
- Department of Information Technology, National Institute of Technology, Srinagar, India
| | - Aditya Gupta
- Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur, India
| | - Mohit Kumar
- Department of Information Technology, National Institute of Technology, Jalandhar, India
| | - Sukhpal Singh Gill
- School of Electronic Engineering and Computer Science, Queen Mary University Of London, UK
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Narasimhan G, Victor A. Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines. J Educ Health Promot 2023; 12:334. [PMID: 38023081 PMCID: PMC10671019 DOI: 10.4103/jehp.jehp_298_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 04/12/2023] [Indexed: 12/01/2023]
Abstract
The word disease is a common word and there are many diseases like heart disease, diabetes, breast cancer, COVID-19, and kidney disease that threaten humans. Data-mining methods are proving to be increasingly beneficial in the present day, especially in the field of medical applications; through the use of machine-learning methods, that are used to extract valuable information from healthcare data, which can then be used to predict and treat diseases early, reducing the risk of human life. Machine-learning techniques are useful especially in the field of health care in extracting information from healthcare data. These data are very much helpful in predicting the disease early and treating the patients to reduce the risk of human life. For classification and decision-making, data mining is very much suitable. In this paper, a comprehensive study on several diseases and diverse machine-learning approaches that are functional to predict those diseases and also the different datasets used in prediction and making decisions are discussed in detail. The drawbacks of the models from various research papers have been observed and reveal countless computational intelligence approaches. Naïve Bayes, logistic regression (LR), SVM, and random forest are able to produce the best accuracy. With further optimization algorithms like genetic algorithm, particle swarm optimization, and ant colony optimization combined with machine learning, better performance can be achieved in terms of accuracy, specificity, precision, recall, and specificity.
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Affiliation(s)
- Geetha Narasimhan
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Akila Victor
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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47
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Shaukat MW, Amin R, Muslam MMA, Alshehri AH, Xie J. A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning. Sensors (Basel) 2023; 23:8070. [PMID: 37836902 PMCID: PMC10575062 DOI: 10.3390/s23198070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/09/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.
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Affiliation(s)
- Muhammad Waqas Shaukat
- Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan
| | - Rashid Amin
- Department of Computer Science, University of Chakwal, Chakwal 48800, Pakistan
| | - Muhana Magboul Ali Muslam
- Department of Information Technology, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh 11432, Saudi Arabia;
| | - Asma Hassan Alshehri
- Durma College of Science and Humanities, Shaqra University, Shaqra 11961, Saudi Arabia
| | - Jiang Xie
- Department of Electrical and Computer Engineering, The University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223, USA
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Espinosa O, Ramos J, Rojas-Botero ML, Fernández-Niño JA. Years of life lost to COVID-19 in 49 countries: A gender- and life cycle-based analysis of the first two years of the pandemic. PLOS Glob Public Health 2023; 3:e0002172. [PMID: 37721925 PMCID: PMC10506703 DOI: 10.1371/journal.pgph.0002172] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 08/22/2023] [Indexed: 09/20/2023]
Abstract
Specific mortality rates have been widely used to monitor the main impacts of the COVID-19 pandemic; however, a more meaningful measure is the Years of Life Lost (YLL) due to the disease, considering it takes into account the premature nature of each death. We estimated the YLL due to COVID-19 between January 2020 and December 2021 in 49 countries for which information was available, developing an analytical method that mathematically refines that proposed by the World Health Organization. We then calculated YLL rates overall, as well as by sex and life cycle. Additionally, we estimated the national cost-effective budgets required to manage COVID-19 from a health system perspective. During the two years of analysis, we estimated that 85.6 million years of life were lost due to COVID-19 in the 49 countries studied. However, due to a lack of data, we were unable to analyze the burden of COVID-19 in about 75% of the countries in the world. We found no difference in the magnitude of YLL rates by gender but did find differences according to life cycle, with older adults contributing the greatest burden of YLL. The COVID-19 pandemic has posed a significant burden of disease, which has varied between countries. However, due to the lack of quality and disaggregated data, it has been difficult to monitor and compare the pandemic internationally. Therefore, it is imperative to strengthen health information systems in order to prepare for future pandemics as well as to evaluate their impacts.
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Affiliation(s)
- Oscar Espinosa
- Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | - Jeferson Ramos
- Economic Models and Quantitative Methods Research Group, Centro de Investigaciones para el Desarrollo, Universidad Nacional de Colombia, Bogotá, D.C., Colombia
| | | | - Julián Alfredo Fernández-Niño
- Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
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Suárez M, Martínez R, Torres AM, Ramón A, Blasco P, Mateo J. A Machine Learning-Based Method for Detecting Liver Fibrosis. Diagnostics (Basel) 2023; 13:2952. [PMID: 37761319 PMCID: PMC10529519 DOI: 10.3390/diagnostics13182952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Cholecystectomy and Metabolic-associated steatotic liver disease (MASLD) are prevalent conditions in gastroenterology, frequently co-occurring in clinical practice. Cholecystectomy has been shown to have metabolic consequences, sharing similar pathological mechanisms with MASLD. A database of MASLD patients who underwent cholecystectomy was analysed. This study aimed to develop a tool to identify the risk of liver fibrosis after cholecystectomy. For this purpose, the extreme gradient boosting (XGB) algorithm was used to construct an effective predictive model. The factors associated with a better predictive method were platelet level, followed by dyslipidaemia and type-2 diabetes (T2DM). Compared to other ML methods, our proposed method, XGB, achieved higher accuracy values. The XGB method had the highest balanced accuracy (93.16%). XGB outperformed KNN in accuracy (93.16% vs. 84.45%) and AUC (0.92 vs. 0.84). These results demonstrate that the proposed XGB method can be used as an automatic diagnostic aid for MASLD patients based on machine-learning techniques.
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Affiliation(s)
- Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Raquel Martínez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Ana María Torres
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Antonio Ramón
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
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50
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Wang S. Point of Interest recommendation for social network using the Internet of Things and deep reinforcement learning. Math Biosci Eng 2023; 20:17428-17445. [PMID: 37920061 DOI: 10.3934/mbe.2023775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Point of Interest (POI) recommendation is one of the important means for businesses to fully understand user preferences and meet their personalized needs, laying a solid foundation for the development of e-commerce and social networks. However, traditional social network POI recommendation algorithms suffer from various problems such as low accuracy and low recall. Therefore, a social network POI recommendation algorithm using the Internet of Things (IoT) and deep reinforcement learning (DRL) is proposed. First, the overall framework of the POI recommendation algorithm is designed by integrating IoT technology and DRL algorithm. Second, under the support of this framework, IoT technology is utilized to deeply explore users' personalized preferences for POI recommendation, analyze the internal rules of user check-in behavior and integrate multiple data sources. Finally, a DRL algorithm is used to construct the recommendation model. Multiple data sources are used as input to the model, based on which the check-in probability is calculated to generate the POI recommendation list and complete the design of the social network POI recommendation algorithm. Experimental results show that the accuracy of the proposed algorithm for social network POI recommendation has a maximum value of 98%, the maximum recall is 97% and the root mean square error is low. The recommendation time is short, and the maximum recommendation quality is 0.92, indicating that the recommendation effect of the proposed algorithm is better. By applying this method to the e-commerce field, businesses can fully utilize POI recommendation to recommend products and services that are suitable for users, thus promoting the development of the social economy.
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Affiliation(s)
- Shuguang Wang
- School of Transportation Information, Jilin Communications Polytechnic, Changchun 130015, China
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