Table 1.

SVM kernel evaluation

Metric	Kernel
	Linear	Polynomial (d = 2)	Polynomial (d = 3)	RBF	Sigmoid
Sensitivity	0.80 ± 0.05	0.80 ± 0.05	0.82 ± 0.03	0.60 ± 0.05	0.00 ± 0.00
Specificity	0.91 ± 0.01	0.96 ± 0.01	0.96 ± 0.01	0.99 ± 0.02	0.84 ± 0.03

Sensitivity = TP/(TP + FN); Specificity = TN/(TN + FP),

where TP, TN, FP, FN correspond to true positive, true negative, false positive and false negative, respectively. We performed three independent runs of 10-fold cross validation on the training collection and reported the average sensitivity/specificity and the standard deviation. The kernel with the best performance in both sensitivity and specificity is highlighted in bold. This is also the kernel we used throughout our analyses.