Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This artic...

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Autores principales:	Kaushalya Dissanayake, Md Gapar Md Johar
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/cd00bdeff7e144b2946f8135de359528
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spelling	oai:doaj.org-article:cd00bdeff7e144b2946f8135de3595282021-11-15T01:19:59ZComparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms1687-973210.1155/2021/5581806https://doaj.org/article/cd00bdeff7e144b2946f8135de3595282021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/5581806https://doaj.org/toc/1687-9732Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.Kaushalya DissanayakeMd Gapar Md JoharHindawi LimitedarticleElectronic computers. Computer scienceQA75.5-76.95ENApplied Computational Intelligence and Soft Computing, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electronic computers. Computer science QA75.5-76.95
spellingShingle	Electronic computers. Computer science QA75.5-76.95 Kaushalya Dissanayake Md Gapar Md Johar Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
description	Heart disease is recognized as one of the leading factors of death rate worldwide. Biomedical instruments and various systems in hospitals have massive quantities of clinical data. Therefore, understanding the data related to heart disease is very important to improve prediction accuracy. This article has conducted an experimental evaluation of the performance of models created using classification algorithms and relevant features selected using various feature selection approaches. For results of the exploratory analysis, ten feature selection techniques, i.e., ANOVA, Chi-square, mutual information, ReliefF, forward feature selection, backward feature selection, exhaustive feature selection, recursive feature elimination, Lasso regression, and Ridge regression, and six classification approaches, i.e., decision tree, random forest, support vector machine, K-nearest neighbor, logistic regression, and Gaussian naive Bayes, have been applied to Cleveland heart disease dataset. The feature subset selected by the backward feature selection technique has achieved the highest classification accuracy of 88.52%, precision of 91.30%, sensitivity of 80.76%, and f-measure of 85.71% with the decision tree classifier.
format	article
author	Kaushalya Dissanayake Md Gapar Md Johar
author_facet	Kaushalya Dissanayake Md Gapar Md Johar
author_sort	Kaushalya Dissanayake
title	Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
title_short	Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
title_full	Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
title_fullStr	Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
title_full_unstemmed	Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms
title_sort	comparative study on heart disease prediction using feature selection techniques on classification algorithms
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/cd00bdeff7e144b2946f8135de359528
work_keys_str_mv	AT kaushalyadissanayake comparativestudyonheartdiseasepredictionusingfeatureselectiontechniquesonclassificationalgorithms AT mdgaparmdjohar comparativestudyonheartdiseasepredictionusingfeatureselectiontechniquesonclassificationalgorithms
_version_	1718428949849047040

Comparative Study on Heart Disease Prediction Using Feature Selection Techniques on Classification Algorithms

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