Integrating traditional Chinese medicine syndromes with clinical variables to predict lower extremity arterial disease in type 2 diabetes

Table 4 Performance comparison of prediction models based on five machine learning classifiers

Model	AUC	Accuracy	Sensitivity	Specificity	F1 score
XGBoost	0.763	0.712	0.651	0.820	0.743
Random forest	0.762	0.699	0.631	0.820	0.728
Logistic regression	0.758	0.686	0.605	0.829	0.711
LightGBM	0.754	0.703	0.641	0.811	0.733
GBDT	0.744	0.699	0.651	0.784	0.734

AUC: Area under the receiver operating characteristic curve; GBDT: Gradient boosting decision tree.