Table 3.

Performance metrics when including the first n important features of each model.

	1 Decision Tree n = 14	2 Random Forest n = 55	3 Gradient Boosting n = 26	4 AdaBoost n = 24
Recall (sensitivity)	0.893 (0.05)	0.926 (0.037)	0.93 (0.024)	0.932 (0.026)
Specificity	0.903 (0.045)	0.949 (0.018)	0.932 (0.046)	0.946 (0.038)
Precision	0.915 (0.036)	0.955 (0.015)	0.942 (0.036)	0.954 (0.032)
F1-score	0.903 (0.03)	0.94 (0.019)	0.936 (0.019)	0.943 (0.023)
Accuracy	0.897 (0.031)	0.937 (0.019)	0.931 (0.022)	0.939 (0.024)
AUC	0.898 (0.031)	0.938 (0.018)	0.931 (0.023)	0.939 (0.025)

The value of n is indicated in the header of each column. For each metric, we present the mean value and standard deviation based on ten-fold cross-validation.