Table 2. The classification results of all the models.

Classifiers	ACC	Sen	Spe	AUC	F1_score	MCC	Log_loss
RF	0.759 (0.074)	0.726 (0.018)	0.791 (0.061)	0.84	0.727	0.511	8.325
LR	0.643 (0.016)	0.619 (0.029)	0.657 (0.045)	0.669	0.53	0.272	12.335
LR-L1	0.670 (0.014)	0.667 (0.020)	0.675 (0.014)	0.656	0.641	0.336	11.41
LR-PCA	0.741 (0.034)	0.644 (0.014)	0.824 (0.078)	0.777	0.688	0.473	8.943
CNN	0.750 (0.029)	0.683 (0.103)	0.807 (0.048)	0.725	0.708	0.492	8.635
CapsNet	0.813 (0.018)	0.822 (0.070)	0.807 (0.048)	0.852	0.796	0.624	6.476

ACC, Sen, and Spe are displayed as mean (standard deviation). ACC, accuracy; Sen, sensitivity; Spe, specificity; AUC, area under the curve; MCC, Matthews correlation coefficient; RF, random forest; LR, logistic regression; PCA, principal component analysis; AUC, area under curve; CNN, convolutional neural network; CapsNet, capsule network.