Table 5. A comprehensive overview of the performance evaluation of different learning models used in this study.

Algorithm	Accuracy	ROC-AUC	F1-Score
Adaboost	0.8285	0.854	0.835
Random forest	0.843	0.887	0.851
SVM	0.841	0.878	0.849
XGBoost	0.846	0.888	0.853
Logistic regression	0.835	0.891	0.842