Comparative Analysis of Logistic Regression and Decision Tree Models for Predicting Heart Disease Outcomes

Cardiovascular disease has become a significant global health issue and remains one of the leading causes of mortality, requiring advanced and often costly detection methods. Heart failure, in particular, poses a severe threat to individuals, contributing to increased morbidity and mortality rates. Therefore, accurate prediction and diagnosis are essential to enable early intervention, timely detection, and effective treatment, reducing the life-threatening risks associated with heart disease-a challenge that persists in medical practice. Individuals diagnosed with or at high risk for cardiovascular disease, due to factors such as hypertension, diabetes, hyperlipidemia, or pre-existing conditions, need prompt identification and efficient management strategies. In this context, machine learning (ML) models play a pivotal role. Our study employed two ML techniques, including Logistic Regression (LR) and Decision Tree (DT), which yielded promising results. A comparative analysis of these algorithms was conducted to evaluate their predictive performance. The findings revealed that the Logistic regression achieved superior accuracy compared to the other model.