HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm

Supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifie...

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Autores principales:	Sarria E. A. Ashri, M. M. El-Gayar, Eman M. El-Daydamony
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Cardiovascular disease supervised machine learning algorithms simple genetic algorithm ensembled model majority voting technique Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/0b5ee02ed5234d3c946bf44c24cbe80f
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id	oai:doaj.org-article:0b5ee02ed5234d3c946bf44c24cbe80f
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spelling	oai:doaj.org-article:0b5ee02ed5234d3c946bf44c24cbe80f2021-11-09T00:01:29ZHDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm2169-353610.1109/ACCESS.2021.3122789https://doaj.org/article/0b5ee02ed5234d3c946bf44c24cbe80f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585496/https://doaj.org/toc/2169-3536Supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifiers using the ensembled model with a majority voting technique to improve prediction accuracy. Furthermore, a proposed preprocessing technique and features selection based on a genetic algorithm is suggested to enhance prediction performance and overall time consumption. In addition, the 10-folds cross-validation technique is used to overcome the overfitting problem. Experiments were performed on a dataset for cardiovascular patients from the UCI Machine Learning Repository. Through a comparative analytical approach, the study results indicated that the proposed ensemble classifier model achieved a classification accuracy of 98.18% higher than the rest of the relevant developments in the study.Sarria E. A. AshriM. M. El-GayarEman M. El-DaydamonyIEEEarticleCardiovascular diseasesupervised machine learning algorithmssimple genetic algorithmensembled modelmajority voting techniqueElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146797-146809 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cardiovascular disease supervised machine learning algorithms simple genetic algorithm ensembled model majority voting technique Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Cardiovascular disease supervised machine learning algorithms simple genetic algorithm ensembled model majority voting technique Electrical engineering. Electronics. Nuclear engineering TK1-9971 Sarria E. A. Ashri M. M. El-Gayar Eman M. El-Daydamony HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm
description	Supervised machine learning algorithms are powerful classification techniques commonly used to build prediction models that help diagnose the disease early. However, some challenges like overfitting and underfitting need to be overcome while building the model. This paper introduces hybrid classifiers using the ensembled model with a majority voting technique to improve prediction accuracy. Furthermore, a proposed preprocessing technique and features selection based on a genetic algorithm is suggested to enhance prediction performance and overall time consumption. In addition, the 10-folds cross-validation technique is used to overcome the overfitting problem. Experiments were performed on a dataset for cardiovascular patients from the UCI Machine Learning Repository. Through a comparative analytical approach, the study results indicated that the proposed ensemble classifier model achieved a classification accuracy of 98.18% higher than the rest of the relevant developments in the study.
format	article
author	Sarria E. A. Ashri M. M. El-Gayar Eman M. El-Daydamony
author_facet	Sarria E. A. Ashri M. M. El-Gayar Eman M. El-Daydamony
author_sort	Sarria E. A. Ashri
title	HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm
title_short	HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm
title_full	HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm
title_fullStr	HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm
title_full_unstemmed	HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm
title_sort	hdpf: heart disease prediction framework based on hybrid classifiers and genetic algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/0b5ee02ed5234d3c946bf44c24cbe80f
work_keys_str_mv	AT sarriaeaashri hdpfheartdiseasepredictionframeworkbasedonhybridclassifiersandgeneticalgorithm AT mmelgayar hdpfheartdiseasepredictionframeworkbasedonhybridclassifiersandgeneticalgorithm AT emanmeldaydamony hdpfheartdiseasepredictionframeworkbasedonhybridclassifiersandgeneticalgorithm
_version_	1718441359048704000

HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm

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