Table 2.

Diagnostic performance of ME_ADC_0–1000, BE_IVIM_D, ME_ADC_{all b}, DKI-D and DKI-K by using RF, L1R-LR, PCA-LR, and SVM, respectively

Maps	AUC (95% CI)
	RF	L1R-LR	PCA-LR	SVM	mAUC
ME-ADC0–1000	0.83 (0.80–0.87)	0.83 (0.79–0.87)	0.76 (0.70–0.81)	0.81 (0.76–0.85)	0.81
BE-IVIM-D	0.85 (0.81–0.89)	0.83 (0.78–0.87)	0.75 (0.70–0.82)	0.80 (0.75–0.85)	0.81
ME-ADCall b	0.84 (0.80–0.87)	0.82 (0.79–0.87)	0.77 (0.70–0.83)	0.79 (0.74–0.85)	0.81
DKI-D	0.83 (0.80–0.86)	0.83 (0.78–0.86)	0.75 (0.74–0.82)	0.80 (0.77–0.84)	0.80
DKI-K	0.84 (0.81–0.89)	0.83 (0.78–0.87)	0.74 (0.70–0.80)	0.79 (0.75–0.85)	0.80

RF: random forest; SVM: support vector machine; PCA: principal component analysis; L1R: L1 regularization; LR: linear regression; mAUC: mean values of AUCs of RF, L1R-LR, PCA-LR and SVM