Table 3.

Accuracy and AUC of the seven machine learning models in the training and test sets.

machine learning models	Accuracy of the training set	Accuracy of the test set	AUC of the training set	AUC of the test set(95%CI)
Logistic regression	0.8298	0.7730	0.8662	0.7837(0.6866,0.8607)
Support vector machine	0.8480	0.8226	0.8577	0.7768(0.6692,0.8557)
LightGBM	0.7994	0.8156	0.8473	0.7962(0.6311,0.8257)
Random forest	0.7964	0.8156	0.8607	0.8011(0.7143,0.8710)
XGBoost	0.7842	0.8511	0.8523	0.7930(0.6931,0.8664)
Gaussian naive Bayes	0.7933	0.8156	0.8561	0.7664(0.6746,0.8519)
K-nearest neighbor algorithms	0.8176	0.8085	0.8809	0.7911(0.6673,0.8527)