COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION

Heart disease remains a leading cause of mortality worldwide, necessitating the development of effective diagnostic tools.Machine learning (ML) has emerged as a powerful approach for predicting heart disease, leveraging vast datasets and complex algorithms to uncover patterns indicative of cardiac conditions.This paper explores the application of various machine learning techniques to predict heart disease, comparing the performance of models such as logistic regression, decision trees, support vector machines, and neural networks.Utilizing a dataset comprising patient health records, including attributes like age, blood pressure, cholesterol levels, and electrocardiogram results, we preprocess the data through normalization and feature selection to enhance model accuracy.Our findings demonstrate that machine learning models, particularly ensemble methods and deep learning architectures, significantly outperform traditional statistical methods, achieving high precision and recall rates.This study underscores the potential of machine learning in early detection and prevention of heart disease, advocating for its integration into clinical practice to improve patient outcomes.Future research should focus on real-time data integration and the development of more sophisticated models to further enhance predictive performance.