Table 2.

Performance of ML Classifiers for Predicting Fibrosis Progression Risk Using Clinical Data, Radiomics Features, and Combined Datasets in T2DM Patients with NAFLD

Data Type	ML classifier	AUC	Precision	Recall	F1 Score	Brier Score
Clinical data	Logit	0.704	0.667	0.211	0.320	0.010
	SVM	0.663	1.000	0.053	0.100	0.010
	RF	0.655	0.571	0.421	0.485	0.097
	XGBoost	0.691	0.750	0.474	0.581	0.087
Radiomics feature	Logit	0.933	0.875	0.737	0.800	0.006
	SVM	0.923	0.857	0.632	0.727	0.002
	RF	0.892	1.000	0.474	0.643	0.009
	XGBoost	0.897	0.750	0.632	0.686	0.003
Combined clinical and radiomics data	Logit	0.937	0.737	0.737	0.737	0.006
	SVM	0.949	0.857	0.862	0.727	0.004
	RF	0.862	0.900	0.474	0.621	0.008
	XGBoost	0.913	0.875	0.737	0.800	0.010