Enhanced Regularized Polynomial XGBoost (ERP-XGB): Reducing Bias and Optimizing Performance in Cardiovascular Risk Prediction
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| Julkaisussa: | ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal vol. 14 (2025), p. e32367-e32389 |
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| Päätekijä: | |
| Julkaistu: |
Ediciones Universidad de Salamanca
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| Aiheet: | |
| Linkit: | Citation/Abstract Full Text - PDF |
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| 001 | 3282913673 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2255-2863 | ||
| 024 | 7 | |a 10.14201/adcaij.32367 |2 doi | |
| 035 | |a 3282913673 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Boughareb, Djalila | |
| 245 | 1 | |a Enhanced Regularized Polynomial XGBoost (ERP-XGB): Reducing Bias and Optimizing Performance in Cardiovascular Risk Prediction | |
| 260 | |b Ediciones Universidad de Salamanca |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Cardiovascular disease | ||
| 653 | |a Recall | ||
| 653 | |a Accuracy | ||
| 653 | |a Regularization | ||
| 653 | |a Datasets | ||
| 653 | |a Bias | ||
| 653 | |a Machine learning | ||
| 653 | |a Heart attacks | ||
| 653 | |a Heart diseases | ||
| 653 | |a Heart failure | ||
| 653 | |a Polynomials | ||
| 773 | 0 | |t ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal |g vol. 14 (2025), p. e32367-e32389 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3282913673/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3282913673/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |