Heart Disease Prediction using XGBoost and Random Forest Models

Heart disease is a prevalent and life-threatening medical condition that continues to pose a significant global health challenge. Early and accurate prediction of heart disease can enable timely interventions and improve patient outcomes. This study presents a comparative analysis of two machine learning models, XG Boost and Random Forest, for the use of an extensive dataset in the prediction of heart disease.