Risk-stratified assessment of 180-day mortality in severe alcoholic hepatitis

Table 3 Performance of the best-machine learning model and traditional scores in the training and test sets

Dataset	Model	AUC	Accuracy	Sensitivity	Specificity	Kappa
Test set	Best-ML (XGBoost)	0.848 (0.813-0.883)	0.785	0.719	0.824	0.541
Training set	Best-ML (XGBoost)	0.837 (0.795-0.880)	0.752	0.733	0.764	0.491
Test set	MELD	0.821 (0.784-0.859)	0.715	0.374	0.917	0.323
Training set	MELD	0.804 (0.757-0.851)	0.693	0.397	0.895	0.314
Test set	MDF	0.787 (0.745-0.829)	0.709	0.380	0.903	0.312
Training set	MDF	0.713 (0.658-0.769)	0.643	0.305	0.874	0.195

AUC: Area under the curve; ML: Machine learning; MDF: Maddrey discriminant function; MELD: Model for end-stage liver disease.