Computer-Aided Decision Support System for Diagnosis of Heart Diseases
Gizeaddis Lamesgin Simegn,1 Worku Birhanie Gebeyehu,2 Mizanu Zelalem Degu2 1School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; 2Faculty of Computing, Jimma Institute of Technology, Jimma University, Jimma, EthiopiaCorrespondence: Gizeaddis Lamesgin Si...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2022-05-01
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| Series: | Research Reports in Clinical Cardiology |
| Subjects: | |
| Online Access: | https://www.dovepress.com/computer-aided-decision-support-system-for-diagnosis-of-heart-diseases-peer-reviewed-fulltext-article-RRCC |
| _version_ | 1811248766172266496 |
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| author | Simegn GL Gebeyehu WB Degu MZ |
| author_facet | Simegn GL Gebeyehu WB Degu MZ |
| author_sort | Simegn GL |
| collection | DOAJ |
| description | Gizeaddis Lamesgin Simegn,1 Worku Birhanie Gebeyehu,2 Mizanu Zelalem Degu2 1School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; 2Faculty of Computing, Jimma Institute of Technology, Jimma University, Jimma, EthiopiaCorrespondence: Gizeaddis Lamesgin Simegn, Tel +251913925481, Email gizeaddis.lamesgin@ju.edu.etBackground: Cardiovascular diseases (CVDs) are the leading causes of death worldwide and the number of people dying from these diseases is steadily increasing. The rapid economic transformation leading to environmental changes and unhealthy lifestyles increase the risk factors and incidence of cardiovascular disease. The limited access to health facilities, lack of expert cardiologists, and lack of regular health check-up trends make CVD a major cause of mortality in low-resource settings. Computer-aided diagnosis using artificial intelligence techniques (AI) can help reduce the mortality rate by providing decision support to experts allowing early diagnosis and treatment.Methods: In this paper, an AI-based computer-aided heart disease diagnosis decision support system has been proposed using clinical data, patient information, and electrocardiogram (ECG) data. The proposed system includes three modules: an ECG processor module that allows cardiologists to process and analyze the different waveforms, a machine learning-based heart disease prediction module based on patient information and clinical data, and a deep learning-based 18 heart conditions multiclass classification module using 12-lead ECG data. A user-friendly user interface has also been developed for ease of use of the proposed techniques.Results: The heart disease prediction module was found to be 100% accurate in predicting heart disease based on clinical and patient information, and the multiclass classification module was 93.27% accurate, on average, in classifying heart conditions based on a 12-lead ECG signal. The ECG processor also provides quick diagnosis by analyzing important ECG waveforms and segments.Conclusion: The proposed system may have the potential for facilitating heart disease diagnosis. The proposed method allows physicians to analyze and predict heart disease easily and early, based on the available resource, improving diagnosis accuracy and treatment planning.Keywords: artificial intelligence, AI, clinical data, diagnosis, ECG signal, heart disease |
| first_indexed | 2024-04-12T15:33:43Z |
| format | Article |
| id | doaj.art-b1f10af5e4d64db283880cc58172468b |
| institution | Directory Open Access Journal |
| issn | 1179-8475 |
| language | English |
| last_indexed | 2024-04-12T15:33:43Z |
| publishDate | 2022-05-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Research Reports in Clinical Cardiology |
| spelling | doaj.art-b1f10af5e4d64db283880cc58172468b2022-12-22T03:27:00ZengDove Medical PressResearch Reports in Clinical Cardiology1179-84752022-05-01Volume 13395475320Computer-Aided Decision Support System for Diagnosis of Heart DiseasesSimegn GLGebeyehu WBDegu MZGizeaddis Lamesgin Simegn,1 Worku Birhanie Gebeyehu,2 Mizanu Zelalem Degu2 1School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia; 2Faculty of Computing, Jimma Institute of Technology, Jimma University, Jimma, EthiopiaCorrespondence: Gizeaddis Lamesgin Simegn, Tel +251913925481, Email gizeaddis.lamesgin@ju.edu.etBackground: Cardiovascular diseases (CVDs) are the leading causes of death worldwide and the number of people dying from these diseases is steadily increasing. The rapid economic transformation leading to environmental changes and unhealthy lifestyles increase the risk factors and incidence of cardiovascular disease. The limited access to health facilities, lack of expert cardiologists, and lack of regular health check-up trends make CVD a major cause of mortality in low-resource settings. Computer-aided diagnosis using artificial intelligence techniques (AI) can help reduce the mortality rate by providing decision support to experts allowing early diagnosis and treatment.Methods: In this paper, an AI-based computer-aided heart disease diagnosis decision support system has been proposed using clinical data, patient information, and electrocardiogram (ECG) data. The proposed system includes three modules: an ECG processor module that allows cardiologists to process and analyze the different waveforms, a machine learning-based heart disease prediction module based on patient information and clinical data, and a deep learning-based 18 heart conditions multiclass classification module using 12-lead ECG data. A user-friendly user interface has also been developed for ease of use of the proposed techniques.Results: The heart disease prediction module was found to be 100% accurate in predicting heart disease based on clinical and patient information, and the multiclass classification module was 93.27% accurate, on average, in classifying heart conditions based on a 12-lead ECG signal. The ECG processor also provides quick diagnosis by analyzing important ECG waveforms and segments.Conclusion: The proposed system may have the potential for facilitating heart disease diagnosis. The proposed method allows physicians to analyze and predict heart disease easily and early, based on the available resource, improving diagnosis accuracy and treatment planning.Keywords: artificial intelligence, AI, clinical data, diagnosis, ECG signal, heart diseasehttps://www.dovepress.com/computer-aided-decision-support-system-for-diagnosis-of-heart-diseases-peer-reviewed-fulltext-article-RRCCartificial intelligenceaiclinical datadiagnosisecg signalheart disease |
| spellingShingle | Simegn GL Gebeyehu WB Degu MZ Computer-Aided Decision Support System for Diagnosis of Heart Diseases Research Reports in Clinical Cardiology artificial intelligence ai clinical data diagnosis ecg signal heart disease |
| title | Computer-Aided Decision Support System for Diagnosis of Heart Diseases |
| title_full | Computer-Aided Decision Support System for Diagnosis of Heart Diseases |
| title_fullStr | Computer-Aided Decision Support System for Diagnosis of Heart Diseases |
| title_full_unstemmed | Computer-Aided Decision Support System for Diagnosis of Heart Diseases |
| title_short | Computer-Aided Decision Support System for Diagnosis of Heart Diseases |
| title_sort | computer aided decision support system for diagnosis of heart diseases |
| topic | artificial intelligence ai clinical data diagnosis ecg signal heart disease |
| url | https://www.dovepress.com/computer-aided-decision-support-system-for-diagnosis-of-heart-diseases-peer-reviewed-fulltext-article-RRCC |
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