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

Table 3 Performance of the models for training set (all features)

Model	Precision	Sensitivity	Specificity	Accuracy	AUROC	F1 score
XGBoost	0.904	1	0.94	0.962	0.988	0.949
ET	0.526	1	0.471	0.667	0.999	0.69
GBDT	0.952	1	0.971	0.981	1	0.976
LR	0.613	0.95	0.647	0.759	0.907	0.745
RF	0.769	1	0.824	0.889	0.996	0.87

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.