Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing
Heart disease is the leading cause of death from chronic diseases in the developing countries. The difficulty of making an accurate and timely diagnosis is exacerbated by a lack of resources and professionals in some areas, which contributes to this reality. Medical professionals may benefit from te...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2021/6718029 |
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| _version_ | 1832555145733341184 |
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| author | Mohammad Alsaffar Abdullah Alshammari Gharbi Alshammari Saud Aljaloud Tariq S. Almurayziq Fadam Muteb Abdoon Solomon Abebaw |
| author_facet | Mohammad Alsaffar Abdullah Alshammari Gharbi Alshammari Saud Aljaloud Tariq S. Almurayziq Fadam Muteb Abdoon Solomon Abebaw |
| author_sort | Mohammad Alsaffar |
| collection | DOAJ |
| description | Heart disease is the leading cause of death from chronic diseases in the developing countries. The difficulty of making an accurate and timely diagnosis is exacerbated by a lack of resources and professionals in some areas, which contributes to this reality. Medical professionals may benefit from technological advancements that aid in the accurate diagnosis of patients. In light of these findings, a hybrid diagnostic tool has been developed that combines several computational intelligence (machine learning) techniques capable of analyzing clinical histories and images of electrocardiogram signals and indicating whether or not the patient has ischemic heart disease with up to 97.01% accuracy. Working with medical experts and a database containing clinical data on approximately 1020 patients and their diagnoses was required for this project. Both were put to use. A picture database containing 92 images of electrocardiogram signals was also used in this project for the analysis of the Artificial Neural Network. After extensive research and testing by the medical community, which supported the project and provided positive feedback, a successful tool was developed. This demonstrated the tool’s effectiveness. |
| format | Article |
| id | doaj-art-2200613262a842bba2a9f085609a6fdf |
| institution | Kabale University |
| issn | 1754-2103 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Bionics and Biomechanics |
| spelling | doaj-art-2200613262a842bba2a9f085609a6fdf2025-02-03T05:49:29ZengWileyApplied Bionics and Biomechanics1754-21032021-01-01202110.1155/2021/6718029Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary ComputingMohammad Alsaffar0Abdullah Alshammari1Gharbi Alshammari2Saud Aljaloud3Tariq S. Almurayziq4Fadam Muteb Abdoon5Solomon Abebaw6University of Ha’ilUniversity of Ha’ilUniversity of Ha’ilUniversity of Ha’ilUniversity of Ha’ilAnalytical ChemistryDepartment of StatisticsHeart disease is the leading cause of death from chronic diseases in the developing countries. The difficulty of making an accurate and timely diagnosis is exacerbated by a lack of resources and professionals in some areas, which contributes to this reality. Medical professionals may benefit from technological advancements that aid in the accurate diagnosis of patients. In light of these findings, a hybrid diagnostic tool has been developed that combines several computational intelligence (machine learning) techniques capable of analyzing clinical histories and images of electrocardiogram signals and indicating whether or not the patient has ischemic heart disease with up to 97.01% accuracy. Working with medical experts and a database containing clinical data on approximately 1020 patients and their diagnoses was required for this project. Both were put to use. A picture database containing 92 images of electrocardiogram signals was also used in this project for the analysis of the Artificial Neural Network. After extensive research and testing by the medical community, which supported the project and provided positive feedback, a successful tool was developed. This demonstrated the tool’s effectiveness.http://dx.doi.org/10.1155/2021/6718029 |
| spellingShingle | Mohammad Alsaffar Abdullah Alshammari Gharbi Alshammari Saud Aljaloud Tariq S. Almurayziq Fadam Muteb Abdoon Solomon Abebaw Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing Applied Bionics and Biomechanics |
| title | Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing |
| title_full | Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing |
| title_fullStr | Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing |
| title_full_unstemmed | Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing |
| title_short | Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing |
| title_sort | machine learning for ischemic heart disease diagnosis aided by evolutionary computing |
| url | http://dx.doi.org/10.1155/2021/6718029 |
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