Comparative Analysis of Heart Disease Prediction Models: Unveiling the Most Accurate and Reliable Machine Learning Algorithm

Heart disease is a significant health concern, warranting accurate prediction models for timely intervention. This research paper presents a comparative analysis of three popular machine learning algorithms, namely Logistic Regression, Support Vector Machines (SVM), and Random Forest, for heart disease prediction. Utilizing a comprehensive dataset encompassing clinical and lifestyle features, each model was developed and evaluated using standard metrics. The study unveils the most accurate and reliable algorithm for heart disease prediction, offering valuable insights into model performance. Furthermore, feature importance analysis sheds light on critical factors influencing accurate predictions. The results aid healthcare professionals in selecting the most appropriate model for efficient heart disease prediction, contributing to improved patient care and clinical decision-making. Random Forest achieved 88% accuracy, outperforming Logistic Regression and SVM for heart disease predictio