Extracellular vesicles as biomarkers for metabolic dysfunction-associated steatotic liver disease staging using explainable artificial intelligence

Table 3 Performance on the CatBoost-C1a (a) and CatBoost-C2h-21 (b)

Model		spec_cv_m	sens_cv_m	roc_cv_m	f1_cv_m
CB-C1a	Mean	0.76	0.66	0.71	0.72
	SD	0.05	0.06	0.03	0.05
	Min	0.65	0.53	0.67	0.64
	25%	0.73	0.63	0.68	0.68
	50%	0.76	0.66	0.71	0.73
	75%	0.79	0.70	0.73	0.75
	Max	0.83	0.74	0.77	0.78
CB-C2h-21	Mean	0.80	0.81	0.81	0.71
	SD	0.06	0.06	0.04	0.05
	Min	0.68	0.74	0.77	0.64
	25%	0.78	0.76	0.78	0.67
	50%	0.81	0.80	0.79	0.69
	75%	0.85	0.85	0.82	0.73
	Max	0.89	0.92	0.89	0.81

Using ten times iterative 5CV and 3CV for Case 1 and Case 2, respectively. Here x_cv_m for x in {spec, sens, roc, f1} corresponds to {specificity, sensitivity, receiver operating characteristic-area under the curve, F1}. roc: Receiver operating characteristic; spec: Specificity; sens: Sensitivity; CB-C2h-21: CatBoost C2h-21; CB-C1a: The CatBoost C1a; C1: Case 1; C2: Case 2; Min: Minimum; Max: Maximum.