Table 4.

Comparison between classification metrics of the different models in the training and validation sets.

Models	Training set				Validation set
	Sensitivity	Specificity	F1-score	AUC	Sensitivity	Specificity	F1-score	AUC
Random forest (RF)	0.98	0.8	0.88	0.89	0.92	0.92	0.92	0.92
Logistic regression (LR)	1	0	0	0.5	0.95	0.81	0.87	0.88
Decision tree (DT)	0.82	0.8	0.81	0.82	0.91	0.66	0.77	0.78
eXtreme gradient boosting (XGB)	0.98	0.8	0.88	0.89	0.93	0.92	0.92	0.93
Voter classifier soft	0.98	0.6	0.74	0.75	0.93	0.88	0.9	0.90
Voter classifier hard	0.95	0.8	0.87	0.88	0.91	0.92	0.91	0.92