Table 2.

Achieved performance of the XGBoost, Linear Regression (LR), Support Vector Machine (SVM), and Decision Tree (DT) models, computed considering the 20-Repeated 10-Fold Cross-Validation. AUC-ROC, area under the receiver operating characteristic curve; PPV, positive predictive value; NPV, negative predictive value.

	Accuracy	AUC-ROC	Sensitivity	Specificity	PPV	NPV
XGBoost	0.707 ± 0.101	0.752 ± 0.107	0.712 ± 0.147	0.704 ± 0.150	0.711 ± 0.119	0.726 ± 0.118
LR	0.660 ± 0.099	0.725 ± 0.107	0.732 ± 0.135	0.592 ± 0.150	0.641 ± 0.102	0.703 ± 0.129
SVM	0.662 ± 0.099	0.713 ± 0.118	0.795 ± 0.165	0.534 ± 0.154	0.626 ± 0.095	0.749 ± 0.160
DT	0.656 ± 0.100	0.661 ± 0.101	0.644 ± 0.154	0.669 ± 0.143	0.660 ± 0.118	0.668 ± 0.114