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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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%. |
format | Online Article Text |
id | pubmed-9573101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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