Cardiovascular Risk Prediction Using Extreme Gradient Boosting: A Machine Learning Approach

Cardiovascular mortality remains a major global health issue. A significant number of deaths could be prevented with timely risk identification. This study presents a machine learning framework called the Heart Attack Prediction System (HAPS), which uses XGBoost as its main predictive engine. The model is trained on a combined set of 12,000 records from two publicly available sources: the UCI Cleveland Heart Disease Dataset and the Kaggle Heart Attack Analysis and Prediction Dataset. In addition