Enhanced Regularized Polynomial XGBoost (ERP-XGB): Reducing Bias and Optimizing Performance in Cardiovascular Risk Prediction

Abstract

Cardiovascular diseases are among the leading causes of death globally, emphasizing the critical need for machine learning models that are both accurate and fair in clinical decision-making. This study introduces the Enhanced Regularized Polynomial XGBoost (ERP-XGB) model, which integrates polynomial feature expansion with L1, L2, and gamma regularization terms to improve classification accuracy, address class imbalance, and reduce algorithmic bias. ERP-XGB was evaluated on four benchmark datasets: Heart Failure (299 samples), Heart Attack (1,319 samples), Heart Disease (917 samples), and BRFSS (253679 samples). On the Heart Attack dataset, ERP-XGB achieved a ROC AUC of 99. 59 ± 0. 21 %, accuracy of 96. 97 ± 0. 49 %, F1 score of 97. 73 ± 0. 43 %, precision of 96. 30 ± 0. 73 %, and recall of 98. 87 ± 0. 47 %, with an average run time of 30. 63 seconds. In terms of fairness, ERP-XGB reported an Equalized Odds (EO) score of 0. 02 ± 0. 01, Disparate Impact (DI) of 0. 96 ± 0. 02, and Demographic Parity (DP) values of 0. 61 ± 0. 01 for the unprivileged group and 0. 64 ± 0. 01 for the privileged group. On the Heart Disease dataset, ERP-XGB demonstrated even stronger performance, achieving a perfect ROC AUC of 100. 00 ± 0. 00 %, accuracy of 98. 60 ± 0. 43 %, F1 score of 98. 58 ± 0. 37 %, precision of 100. 00 ± 0. 00 %, and recall of 97. 29 ± 0. 48 %, with a run time of 41. 45 seconds. Fairness evaluation showed EO at 0. 03 ± 0. 01, DI at 1. 78 ± 0. 03, and DP values of 0. 69 ± 0. 01 for the unprivileged group and 0. 38 ± 0. 01 for the privileged group. For Heart Failure, ERP-XGB achieved 89. 82±0. 02 % ROC AUC, 82. 93±0. 03 % accuracy, and strong fairness (DI=0. 91±0. 31). On BRFSS, it attained 90. 57±0. 000 % accuracy but showed lower recall (11. 89±0. 004 %) and fairness challenges (DI=0. 38±0. 03). These results confirm that ERP-XGB offers an effective balance between high predictive performance and robust fairness in clinical datasets, making it a promising tool for equitable cardiovascular disease diagnosis.