Heart Attack Prediction: A Comparative Analysis of Supervised Machine Learning Algorithms for Early Detection and Risk Stratification

Abstract

—Heart attacks are a prominent source of morbidity and mortality globally, demanding the development of precise and efficient predictive models for early identification and risk stratification. This paper gives a complete review of supervised machine learning algorithms to measure their efficacy in predicting MI risk, allowing for timely medical intervention. A wide range of clinical data were used to train and verify six classification models. Based on accuracy, precision, recall, and F1-score, LR had the best prediction performance with an accuracy of 85.71%, followed by KNN (84.62%), SVM (80.22%), RF (79.12%), XGBoost (79.12%), and DT (71.43%). The performance of LR demonstrates its practical applicability and interpretability for clinical use, making it a viable candidate for clinical deployment. These findings emphasize the potential of machine learning-driven technologies in improving MI prediction, therefore leading to increased early diagnosis and individualized patient treatment. The study further emphasizes the need for integrating transparent and interpretable ML models into healthcare systems to facilitate informed clinical decision-making and optimize patient outcomes.