Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting

Abstract

Heart attack prediction is a vital component of cardiovascular healthcare, aiming to identify individuals at risk for timely intervention and improved patient outcomes. Despite significant advancements in predictive modeling techniques, several challenges persist, including algorithmic limitations, interpretability issues, data dependence, and scalability concerns. These challenges underscore the need for robust, interpretable, and generalizable predictive models capable of handling the complexities of medical data effectively. In this study, we propose a novel approach leveraging the eXtreme Gradient Boosting (XGBoost) algorithm for heart attack analysis and prediction. We conducted a comprehensive analysis of heart disease datasets, employing rigorous data preprocessing, feature selection, and hyperparameter optimization techniques to develop a highly accurate and interpretable predictive model. Our results demonstrate the efficacy of the XGBoost algorithm in capturing intricate patterns from medical data, achieving superior predictive performance across various metrics. The proposed model addresses the existing challenges in heart attack prediction, offering a promising solution for enhancing cardiovascular healthcare outcomes.