Table 2.

Performance Metrics of Machine Learning Models.

Model	AUROC	Sensitivity	Specificity
Logistic Regression	0.692	0.7236	0.7161
MLP	0.664	0.6829	0.6897
Random Forest	0.765	0.7398	0.7240
CATBoost	0.763	0.7073	0.7145
EBM	0.780	0.7154	0.7198
GBM	0.791	0.7821	0.9652

AUC (Area Under the Curve) = The area under the ROC curve, MLP = Multi-Layer Perceptron, CATBoost = Categorical Boosting, EBM = Explainable Boosting Machine, GBM = Gradient Boosting Machine.