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Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of t...

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Autores principales: Hassan, Ch. Anwar ul, Iqbal, Jawaid, Irfan, Rizwana, Hussain, Saddam, Algarni, Abeer D., Bukhari, Syed Sabir Hussain, Alturki, Nazik, Ullah, Syed Sajid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573101/
https://www.ncbi.nlm.nih.gov/pubmed/36236325
http://dx.doi.org/10.3390/s22197227
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author Hassan, Ch. Anwar ul
Iqbal, Jawaid
Irfan, Rizwana
Hussain, Saddam
Algarni, Abeer D.
Bukhari, Syed Sabir Hussain
Alturki, Nazik
Ullah, Syed Sajid
author_facet Hassan, Ch. Anwar ul
Iqbal, Jawaid
Irfan, Rizwana
Hussain, Saddam
Algarni, Abeer D.
Bukhari, Syed Sabir Hussain
Alturki, Nazik
Ullah, Syed Sajid
author_sort Hassan, Ch. Anwar ul
collection PubMed
description Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
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spelling pubmed-95731012022-10-17 Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers Hassan, Ch. Anwar ul Iqbal, Jawaid Irfan, Rizwana Hussain, Saddam Algarni, Abeer D. Bukhari, Syed Sabir Hussain Alturki, Nazik Ullah, Syed Sajid Sensors (Basel) Article Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%. MDPI 2022-09-23 /pmc/articles/PMC9573101/ /pubmed/36236325 http://dx.doi.org/10.3390/s22197227 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hassan, Ch. Anwar ul
Iqbal, Jawaid
Irfan, Rizwana
Hussain, Saddam
Algarni, Abeer D.
Bukhari, Syed Sabir Hussain
Alturki, Nazik
Ullah, Syed Sajid
Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
title Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
title_full Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
title_fullStr Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
title_full_unstemmed Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
title_short Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
title_sort effectively predicting the presence of coronary heart disease using machine learning classifiers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573101/
https://www.ncbi.nlm.nih.gov/pubmed/36236325
http://dx.doi.org/10.3390/s22197227
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