Artificial Intelligence for Heart Disease Risk Prediction: A Machine Learning Approach

Heart disease remains a leading cause of death globally, underscoring the need for early detection to improve outcomes and reduce healthcare cost. This study proposes a machine learning-based approach to predict heart disease risk using a comprehensive dataset covering demographic, lifestyle, and clinical variables. The study addresses limitations in prior research by focusing on fairness, interpretability, and practical deployment for clinical utility. The methodological framework involved stratified data splits and careful oversampling to prevent leakage. The anonymised Kaggle/BRFSS dataset was partitioned into 70 % training, 15 % validation and 15 % test sets, and SMOTE oversampling was applied only on the training split to balance the minority (heart disease) class. Feature selection via mutual information highlighted HadAngina, BMI and AgeCategory. Hyper-parameter-tuned models-including Logistic Regression, Random Forest, XGBoost, K-nearest neighbour and multilayer perceptron-were trained using stratified 5-fold crossvalidation with fixed random seeds; XGBoost achieved the highest performance (accuracy 91.99%, ROC-AUC 0.9756). Fairness was assessed using demographic-parity difference, equal-opportunity (true-positive-rate) gap and calibration error across sex, age and the official race/ethnicity categories (White, Black, Asian, Hispanic and Other). Initial results revealed lower recall for younger and Black individuals. A simple re-weighting of the training data and group-specific threshold adjustments reduced the demographic-parity difference to 0.0875 with minimal impact on accuracy. SHAP values provided global and local explanations of feature contributions-crucial for building trust among clinicians and patients. The model was deployed via a Flask-based web app, providing real-time heart disease risk assessments with user-friendly interfaces and explanations for clinicians and the public. This study bridges AI and healthcare by delivering a fair, transparent, and accessible predictive tool. Future enhancements include adversarial debiasing, wearable data integration, and expanding to other cardiovascular conditions to improve diagnostic accuracy, reduce inequities, and increase trust in AIdriven healthcare.