Comparison and development of machine learning for thalidomide-induced peripheral neuropathy prediction of refractory Crohn’s disease in Chinese population

Table 4 Performance of the models for testing set (all features)

Model	Precision	Sensitivity	Specificity	Accuracy	AUROC	F1 score
XGBoost	0.75	0.75	0.857	0.818	0.889	0.75
ET	0.667	1	0.667	0.8	0.833	0.8
GBDT	0.667	1	0.667	0.8	0.87	0.8
LR	0.8	0.667	0.889	0.8	0.741	0.727
RF	0.625	0.833	0.667	0.733	0.907	0.714

AUROC: Area under the receiver operating characteristic; XGBoost: Extreme gradient boosting; ET: Extremely random trees; GBDT: Gradient boosting decision tree; LR: Logistic regression; RF: Random forest.