Table 2. Evaluation table of each classifier algorithm prediction model in the training set.

Model	Training set						Test set
	MSE	R 2	Sensitivity	Specificity	Accuracy		Sensitivity	Specificity	Accuracy
Abbreviation: MSE=mean squared error; LR=logistic regression; RF=random forest; SVM=support vector machines; MLP=multilayer perceptron.
LR	0.229	0.086	0.702	0.601	0.627		0.629	0.583	0.591
C5.0	0.197	0.215	0.734	0.730	0.732		0.644	0.689	0.665
RF	0.165	0.342	0.891	0.712	0.779		0.536	0.656	0.665
SVM	0.121	0.517	0.888	0.831	0.859		0.659	0.711	0.683
MLP	0.233	0.073	0.662	0.581	0.602		0.621	0.596	0.624