Table 2.

Cross-validated goodness-of-fit metrics for linear classifiers.

	AUC	Sensitivity	Specificity	Accuracy	Precision	Recall	F1-score	Training size	#Features
MKL-Linear	0.90(0.02)	0.86(0.03)	0.78(0.03)	0.80(0.02)	0.63(0.03)	0.86(0.03)	0.72(0.03)	80%	5
SVM-Linear	0.93(0.02)	0.93(0.03)	0.77(0.03)	0.81(0.02)	0.64(0.03)	0.93(0.02)	0.75(0.03)	80%	65
GLM-Elastic Net	0.92(0.02)	0.91(0.02)	0.76(0.02)	0.81(0.02)	0.62(0.03)	0.91(0.04)	0.74(0.03)	80%	25

Here we list the goodness-of-fit metrics (AUC, sensitivity, specificity, accuracy, precision, recall, and F1-score) obtained for the test dataset (20% of the whole dataset), using the subset of features that provided the most generalizable result, as shown in Fig. 4. Their average values and standard deviations were computed using a tenfold stratified cross-validation.