Table 2.

Model performance with imputed imbalanced training data set DMI(ITD) and validation data set DMI(VD)

	Training in ITD (n = 1029)			Validation in VD (n = 1029)
Classifier	Specificity (TNR)	Sensitivity (TPR)	AUC	Specificity (TNR)	Sensitivity (TPR)	AUC	Rank
(K = 1) NN	0.908	0.167	0.548	0.923	0.292	0.607	9
(K = 3) NN	0.975	0.094	0.601	0.979	0.125	0.627	8
(K = 5) NN	0.985	0.042	0.624	0.989	0.063	0.651	6
(K = 7) NN	0.996	0.031	0.648	0.998	0.052	0.644	7
(K = 9) NN	0.999	0.031	0.660	0.999	0.042	0.665	5
ANN	0.945	0.198	0.694	0.953	0.177	0.676	4
C4.5	0.985	0.083	0.575	0.979	0.125	0.496	12
LMT	0.996	0.010	0.578	0.995	0.042	0.746	1
LR	0.910	0.188	0.567	0.959	0.135	0.596	10
NB	0.810	0.438	0.697	0.833	0.500	0.737	3
SVM	0.966	0.156	0.561	0.976	0.146	0.561	11
RF	0.998	0.021	0.725	0.999	0.010	0.742	2

Abbreviations: ANN = artificial neural network; AUC = area under the curve; C4.5 = decision tree; DMI = decision-tree based missing value imputation; ITD = imbalanced training; KNN = K-nearest neighbor; LMT = logistic model tree; LR = logistic regression; NB = naïve Bayes; RF = random forest; SVM = support vector machine; TNR = true negative rate; TPR = true positive rate; VD = validation.