Article Information

Abstract

Cardiovascular disease remains the leading cause of mortality worldwide, highlighting the need for accurate and early risk prediction to support effective clinical intervention. This study presents a customized Random Forest classifier for ten-year coronary heart disease prediction using the Framingham Heart Study dataset. To address the class imbalance inherent in the data, class- weighted learning is employed while deliberately avoiding synthetic data generation to maintain the original data distribution. The proposed model achieved an overall accuracy, precision, recall, and F1-score of 81%, with a ROC–AUC of 72%, indicating moderate discriminative ability. SHAP-based explainability analysis identified age and systolic blood pressure as the most influential predictors, followed by sex, daily cigarette consumption, and a history of hypertension, aligning with established cardiovascular risk factors. However, class-wise evaluation revealed limited sensitivity for the positive class, with a recall of 34% and a precision of 37%, reflecting the challenges posed by the severe class imbalance in the Framingham dataset. In contrast, the model demonstrated strong performance in identifying negative cases, achieving 88% precision and 90% recall. By integrating predictive modelling with clinical interpretability, this study demonstrates the applicability of interpretable machine learning for healthcare settings where transparency and clinical relevance are essential.