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

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Pubblicato in:ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal vol. 14 (2025), p. e32367-e32389
Autore principale: Boughareb, Djalila
Pubblicazione:
Ediciones Universidad de Salamanca
Soggetti:
Cardiovascular disease
Recall
Accuracy
Regularization
Datasets
Bias
Machine learning
Heart attacks
Heart diseases
Heart failure
Polynomials
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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.
ISSN:2255-2863
DOI:10.14201/adcaij.32367
Fonte:Advanced Technologies & Aerospace Database