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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Autores principales:	Mohammad Alsaffar, Abdullah Alshammari, Gharbi Alshammari, Saud Aljaloud, Tariq S. Almurayziq, Fadam Muteb Abdoon, Solomon Abebaw
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/2200613262a842bba2a9f085609a6fdf
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spelling	oai:doaj.org-article:2200613262a842bba2a9f085609a6fdf2021-11-29T00:56:22ZMachine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing1754-210310.1155/2021/6718029https://doaj.org/article/2200613262a842bba2a9f085609a6fdf2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6718029https://doaj.org/toc/1754-2103Heart 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.Mohammad AlsaffarAbdullah AlshammariGharbi AlshammariSaud AljaloudTariq S. AlmurayziqFadam Muteb AbdoonSolomon AbebawHindawi LimitedarticleBiotechnologyTP248.13-248.65Biology (General)QH301-705.5ENApplied Bionics and Biomechanics, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5
spellingShingle	Biotechnology TP248.13-248.65 Biology (General) QH301-705.5 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
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
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
title	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_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_sort	machine learning for ischemic heart disease diagnosis aided by evolutionary computing
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/2200613262a842bba2a9f085609a6fdf
work_keys_str_mv	AT mohammadalsaffar machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing AT abdullahalshammari machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing AT gharbialshammari machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing AT saudaljaloud machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing AT tariqsalmurayziq machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing AT fadammutebabdoon machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing AT solomonabebaw machinelearningforischemicheartdiseasediagnosisaidedbyevolutionarycomputing
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Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing

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