Title: Detection of coronary artery disease using machine learning algorithms

Authors: Kriti Vashistha; Anuja Bokhare

Addresses: Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India ' Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India

Abstract: Every minute, roughly one person dies from heart disease in the modern era. The heart is the second most important organ in the human body after the brain. Predicting the occurrence of heart diseases is the most important work in the medical industry. This is where machine learning and data analytics comes into play. In this study, the proposed technique analyses three different algorithms: decision trees, random forests, and logistic regression. After correctly training and evaluating the models, we noticed that random forest had the highest accuracy of 83%, followed by logistic regression with 81%, and decision tree with 77%. The most important factors in prediction were found to be age, Trestbps, cholesterol, and Oldpeak. For future work we would enhance the accuracy of our model which will hopefully one day be able to help battle the ever-growing problem of coronary artery disease.

Keywords: medical industry; heart disease; random forest; decision tree; logistic regression; machine learning.

DOI: 10.1504/IJMIC.2023.132578

International Journal of Modelling, Identification and Control, 2023 Vol.43 No.2, pp.83 - 91

Received: 12 Nov 2021
Accepted: 31 Mar 2022
Published online: 30 Jul 2023 *

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