Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes

Table 4 The average performance of the LR, random forest, stellate ganglion block, classification and regression tree, and eXtreme gradient boosting methods

	Accuracy	Sensitivity	Specificity	AUC
LGR	0.685 ± 0.072	0.687 ± 0.152	0.683 ± 0.114	0.703 ± 0.057
CART	0.541 ± 0.074	0.546 ± 0.078	0.529 ± 0.670	0.540 ± 0.070
RF	0.707 ± 0.047	0.711 ± 0.100	0.678 ± 0.099	0.707 ± 0.037
XGBoost	0.712 ± 0.072	0.727 ± 0.139	0.674 ± 0.088	0.719 ± 0.062
NB	0.692 ± 0.059	0.702 ± 0.116	0.669 ± 0.090	0.704 ± 0.056

AUC: Area under the curve; LGR: Logistic regression; CART: Classification and regression tree; RF: Random forest; XGBoost: eXtreme gradient boosting; NB: Naïve Byes.