Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers
Ch. Anwar ul Hassan,
Jawaid Iqbal,
Rizwana Irfan,
Saddam Hussain,
Abeer D. Algarni,
Syed Sabir Hussain Bukhari,
Nazik Alturki,
Syed Sajid Ullah
Affiliations
Ch. Anwar ul Hassan
Department of Creative Technologies, Air University Islamabad, Islamabad 44000, Pakistan
Jawaid Iqbal
Department of Computer Science, Capital University of Science and Technology, Islamabad 44000, Pakistan
Rizwana Irfan
Department of Computer Science, University of Jeddah, P.O. Box 123456, Jeddah 21959, Saudi Arabia
Saddam Hussain
School of Digital Science, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei
Abeer D. Algarni
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Syed Sabir Hussain Bukhari
Department of Electrical Engineering, Sukkur IBA University, Sukkur 65200, Pakistan
Nazik Alturki
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Syed Sajid Ullah
Department of Information and Communication Technology, University of Agder (UiA), N-4898 Grimstad, Norway
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%.
