From Data to Diagnosis: Applying Machine Learning Model for Reliable Heart Disease Prediction

Heart disease remains a significant global health challenge, demanding precise and interpretable predictive tools to aid early intervention and improve patient outcomes. This study applies the XGBoost machine learning algorithm to develop a robust model for predicting cardiovascular disease risk. Leveraging a comprehensive dataset that includes demographic, lifestyle, and clinical features, our approach combines extensive data preprocessing, feature engineering, and Bayesian hyperparameter tuning to achieve superior predictive accuracy, with an AUC of 0.98 and an accuracy of 97.48%. Key predictive factors, identified through SHAP (SHapley Additive exPlanations) values, include cholesterol, blood pressure, and physical activity, offering actionable insights for clinicians. The model's interpretability supports its potential as a reliable, real-time diagnostic tool, enhancing clinical decision-making and risk assessment. This research advances predictive healthcare analytics by showcasing the effectiveness of sophisticated machine learning methods, including XGBoost, for precise and interpretable heart disease forecasting. Subsequent study will seek to validate the model across varied populations and incorporate it into clinical environments for adaptive, individualized cardiovascular risk management.