Table 2.

Classification models performance metrics.

	1 Decision Tree	2 Random Forest	3 Gradient Boosting	4 AdaBoost
Recall (sensitivity)	0.890 (0.035)	0.924 (0.029)	0.924 (0.02)	0.939 (0.029)
Specificity	0.859 (0.039)	0.937 (0.031)	0.932 (0.051)	0.934 (0.052)
Precision	0.880 (0.029)	0.945 (0.026)	0.942 (0.042)	0.944 (0.042)
F1-score	0.885 (0.019)	0.934 (0.02)	0.932 (0.021)	0.941 (0.029)
Accuracy	0.876 (0.02)	0.930 (0.022)	0.928 (0.024)	0.937 (0.032)
AUC	0.875 (0.02)	0.930 (0.022)	0.928 (0.025)	0.937 (0.033)

This table shows the predictive performance across four classification models (1) Decision tree, (2) Random Forest, (3) Gradient Boosting, (4) AdaBoost. For each metric we present the mean value and standard deviation based on ten-fold cross-validation.