Table 3.

Model validation set evaluation metrics.

Model	Accuracy	Sensitivity	Specificity	AUC
Logistic Regression	0.736(0.706–0.764)	0.742(0.551–0.875)	0.735(0.705–0.764)	0.816(0.757–0.875)
Random Forest	0.827(0.802–0.851)	0.451(0.278–0.637)	0.840(0.814–0.863)	0.784(0.726–0.843)
BP-Network	0.666(0.634–0.696)	0.774(0.585–0.897)	0.662(0.630–0.693)	0.779(0.701–0.858)
SVM	0.799(0.772–0.825)	0.710(0.518–0.851)	0.802(0.775–0.828)	0.825(0.770–0.881)
XGBoost	0.750(0.721–0.777)	0.581(0.393–0.749)	0.756(0.726–0.783)	0.756(0.685–0.827)
Naive Bayes	0.729(0.699–0.758)	0.710(0.518–0.851)	0.730(0.699–0.758)	0.803(0.736–0.870)