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Sensors
Volume 22
Issue 19
10.3390/s22197227
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Article

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

by
Ch. Anwar ul Hassan
1,
Jawaid Iqbal
2,
Rizwana Irfan
3,
Saddam Hussain
4,*,
Abeer D. Algarni
5,
Syed Sabir Hussain Bukhari
6,*,
Nazik Alturki
7 and
Syed Sajid Ullah
8,*
1
Department of Creative Technologies, Air University Islamabad, Islamabad 44000, Pakistan
2
Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan
3
Department of Computer Science, University of Jeddah, P.O. Box 123456, Jeddah 21959, Saudi Arabia
4
School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
5
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
6
Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
7
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
8
Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
*
Authors to whom correspondence should be addressed.
Sensors 2022, 22(19), 7227; https://doi.org/10.3390/s22197227
Submission received: 7 April 2022 / Revised: 3 June 2022 / Accepted: 27 July 2022 / Published: 23 September 2022
(This article belongs to the Special Issue AI-Enabled Smart Sensing Technologies for Human-Centered Healthcare Applications)
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Abstract

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%.
Keywords: heart disease dataset; disease prediction; supervised learning; machine learning heart disease dataset; disease prediction; supervised learning; machine learning

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MDPI and ACS Style

Hassan, C.A.u.; Iqbal, J.; Irfan, R.; Hussain, S.; Algarni, A.D.; Bukhari, S.S.H.; Alturki, N.; Ullah, S.S. Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers. Sensors 2022, 22, 7227. https://doi.org/10.3390/s22197227

AMA Style

Hassan CAu, Iqbal J, Irfan R, Hussain S, Algarni AD, Bukhari SSH, Alturki N, Ullah SS. Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers. Sensors. 2022; 22(19):7227. https://doi.org/10.3390/s22197227

Chicago/Turabian Style

Hassan, Ch. Anwar ul, Jawaid Iqbal, Rizwana Irfan, Saddam Hussain, Abeer D. Algarni, Syed Sabir Hussain Bukhari, Nazik Alturki, and Syed Sajid Ullah. 2022. "Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers" Sensors 22, no. 19: 7227. https://doi.org/10.3390/s22197227

APA Style

Hassan, C. A. u., Iqbal, J., Irfan, R., Hussain, S., Algarni, A. D., Bukhari, S. S. H., Alturki, N., & Ullah, S. S. (2022). Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers. Sensors, 22(19), 7227. https://doi.org/10.3390/s22197227

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