Table 2.

Model comparison using training data to predict testing data (n = 106).

Model	True negative	False-positive	False-negative	True positive	Sensitivity	Specificity	Accuracy	Balanced Accuracy
A.Raw data
CNN	75	29	0	2	1	0.72	0.73	0.86
ANN	66	38	0	2	1	0.63	0.64	0.82
Forest(J48)	82	22	1	1	0.5	0.78	0.78	0.64
Random forest	88	16	0	2	1	0.85	0.85	0.93
Random tree	78	26	1	1	0.5	0.75	0.75	0.63
REPT tree	51	53	0	2	1	0.49	0.50	0.75
BayesNet	85	19	0	2	1	0.82	0.82	0.91
Naïve Bayes	85	19	0	2	1	0.82	0.82	0.91
Logistic	22	82	1	1	0.5	0.21	0.23	0.36
SMO	92	12	0	2	1	0.89	0.89	0.95
Median								0.84
B. Normalization data
CNN	48	56	0	2	1	0.46	0.47	0.73
ANN	62	42	0	2	1	0.6	0.60	0.80
Forest(J48)	81	23	1	1	0.5	0.78	0.77	0.64
Random forest	82	22	0	2	1	0.79	0.79	0.90
Random tree	88	16	0	2	1	0.85	0.85	0.93
REPT tree	73	31	0	2	1	0.7	0.71	0.85
BayesNet	86	18	0	2	1	0.83	0.83	0.92
Naïve Bayes	86	18	0	2	1	0.83	0.83	0.92
Logistic	75	29	0	2	1	0.72	0.73	0.86
SMO	91	10	1	1	0.5	0.9	0.9	0.70
Median								0.86
C. From Ko et al.
XGBoost	80	24	0	2	1	0.77	0.77	0.88
AdaBoost	81	23	0	2	1	0.78	0.78	0.89
Random forest	87	17	0	2	1	0.84	0.84	0.92
Deep neural network	95	9	1	1	0.5	0.91	0.91	0.71
DNN + XGBoost	80	24	0	2	1	0.77	0.77	0.88
DNN + AdaBoost	96	8	1	1	0.5	0.92	0.92	0.71
Yan et al model	36	68	0	2	1	0.35	0.36	0.67
EDRnet	95	9	0	2	1	0.91	0.92	0.96
Median								0.88

^∗The 95% CIs of AUC (=0.9) = $1.96\times \sqrt{0.9\times 0.1}/106=0.057$ for the testing dataset.