Title: Diagnosing heart attack risk with logistic regression and decision tree algorithms

Authors: Asli Orgerim; Adnan Kalkan

Addresses: Management Information Systems Department, Bucak Zeliha Tolunay School of Applied Technology and Business, Burdur Mehmet Akif Ersoy University, Burdur, Turkey ' Management Information Systems Department, Bucak Zeliha Tolunay School of Applied Technology and Business, Burdur Mehmet Akif Ersoy University, Burdur, Turkey

Abstract: The number of people who lose their lives due to heart attacks around the world and in our country is increasing day by day. Treatment and early intervention are important for people who have a heart attack and have a chance of survival. When immediate medical attention is provided and appropriate treatment is administered, the survival rate increases. For this reason, this study aimed to diagnose the risk of heart attack early by using machine learning methods. The dataset used includes 303 patient data and 14 features. The data was trained using logistic regression and decision tree algorithms. As a result of the training, a success rate of 83.8% and 77% was achieved, respectively. The logistic regression model gave the better success result.

Keywords: machine learning; classification; logistic regression; decision tree.

DOI: 10.1504/IJHTM.2024.149019

International Journal of Healthcare Technology and Management, 2024 Vol.21 No.3/4, pp.183 - 190

Published online: 09 Oct 2025 *

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