Predicting Heart Attack Risk Using Machine Learning: A Comparative Study of Logistic Regression and Random Forest Algorithms

<p><span>Accurately predicting heart attacks using machine learning algorithms represents a significant advancement in medical diagnostics, enabling enhanced early detection and preventive strategies. This study focuses on developing and evaluating predictive models based on Logistic Regression and Random Forest algorithms to identify individuals at high risk of heart attacks. Utilisng a comprehensive dataset containing critical features such as age, gender, blood pressure, cholesterol levels, and other clinical indicators, the models were trained and validated through a robust data-splitting methodology. Logistic Regression was employed to analyse linear relationships between predictors and heart attack likelihood, providing interpretable coefficients highlighting each feature's significance. Additionally, Random Forest was utilised to capture complex, non-linear interactions among variables, aiming to improve predictive accuracy. This research incorporates 12 key medical parameters: age, sex, chest pain type, resting blood pressure, cholesterol levels, obesity, fasting blood sugar, resting electrocardiogram results, maximum heart rate, and exercise-induced angina. The findings demonstrate the potential of machine learning techniques to enhance heart disease risk prediction, contributing to more effective clinical decision-making and preventive care.</span></p>