Table 1.

Comparison of model accuracy using normalized training data (n = 361).

Model	Sensitivity	Specificity	Accuracy	Balanced accuracy	AUC∗
A. Raw data
CNN	0.90	0.86	0.88	0.88	0.85
ANN	0.95	0.88	0.91	0.92	0.89
Forest (J48)	0.99	0.99	0.99	0.99	0.99
Random forest	1.00	1.00	1.00	1.00	1.00
Random tree	1.00	1.00	1.00	1.00	1.00
REPT tree	0.88	0.91	0.89	0.90	0.88
BayesNet	0.92	0.92	0.92	0.92	0.90
Naïve Bayes	0.78	0.95	0.87	0.87	0.86
Logistic	0.94	0.92	0.93	0.93	0.91
SMO	0.89	0.91	0.09	0.90	0.88
Median				0.92
B. Normalized data
CNN	0.93	0.93	0.92	0.93	0.91
ANN	0.95	0.98	0.97	0.97	0.96
Forest (J48)	0.99	0.97	0.98	0.98	0.97
Random forest	1.00	1.00	1.00	1.00	1.00
Random tree	1.00	1.00	1.00	1.00	1.00
REPT tree	0.92	0.93	0.93	0.93	0.91
BayesNet	0.92	0.92	0.92	0.92	0.90
Naïve Bayes	0.80	0.92	0.87	0.86	0.84
Logistic	0.93	0.92	0.93	0.93	0.91
SMO	0.87	0.93	0.91	0.90	0.88
Median				0.93
C. Ko et al[1]
Random forest	0.89	0.89	0.89	0.89	0.87
Deep neural network	0.91	0.93	0.92	0.92	0.90
EDRnet	0.92	0.93	0.93	0.93	0.91
Median				0.92

^∗The 95% CIs of AUC (=0.9) = $1.96\times \sqrt{0.9\times 0.1}/361$ = 0.03 for the training dataset.