Table 3.

Model performance in training and testing data for the best selected classification models for each dataset. PPV = Positive predictive value. NPV = Negative predictive value. CI = confidence interval (95%).

Model	Method	Train accuracy	Train Kappa	Test Accuracy	Test Kappa	Test Sensitivity	Test Specificity	Test PPV	Test NPV	Tuning parameter	Rank
E-Clinical (N = 284) First Model	Random Forest	0.608[95% CI = 0.598–0.618]	0.052 [95% CI = 0.028–0.075]	0.586	−0.022	0.61	0.33	0.91	0.07	Mtry = 2	1
E-Clinical (N = 284) Second Model	Generalised Linear model	0.574 [95% CI = 0.561–0.587]	0.056[95% CI = 0.028–0.083]	0.600	0.132	0.66	0.48	0.72	0.41	Alpha = 0.1, lambda = 0.019	(1)
A-Clinical, genetic, Expression (N = 108)	K-Nearest Neighbours (KNN)	0.591 [95% CI = 0.556–0.625]	0.096 [95% CI = 0.022–0.170]	0.577	0.077	0.60	0.50	0.80	0.27	K = 9	= 2
B-Clinical, genetic (N = 108)	KNN	0.591 [95% CI = 0.556–0.625]	0.096 [95% CI = 0.022–0.170]	0.577	0.077	0.60	0.50	0.80	0.27	K = 9	= 2
D-Clinical, Expression (N = 108)	KNN	0.591 [95% CI = 0.556–0.625]	0.096 [95% CI = 0.022–0.170]	0.577	0.077	0.60	0.50	0.80	0.27	K = 9	= 2
C-Clinical N = 108	KNN	0.591 [95% CI = 0.556–0.625]	0.096 [95% CI = 0.022–0.170]	0.577	0.077	0.60	0.50	0.80	0.27	K = 9	= 2