Machine learning-based models for advanced fibrosis in non-alcoholic steatohepatitis patients: A cohort study

Table 3 Diagnostic performance of the Extreme Gradient Boosting model and other non-invasive diagnosis models for advanced liver fibrosis

Cohort	Models	Accuracy	Area under receiver operating characteristic curve	Sensitivity	Specificity	Positive predictive value	Negative predictive value	P value
Training	XGB	0.810 (0.764-0.841)	0.934 (0.914-0.955)	0.958	0.744	0.786	0.914
	XGB + LSM	0.844 (0.7815-0.898)	0.977 (0.966-0.980)	0.985	0.758	0.837	0.929	< 0.001
	APRI	0.715 (0.674-0.753)	0.803 (0.765-0.841)	0.907	0.402	0.711	0.727	< 0.001
	FIB-4	0.734 (0.774-0.848)	0.811 (0.774-0.848)	0.898	0.467	0.732	0.738	< 0.001
Validation	XGB	0.794 (0.765-0.862)	0.917 (0.880-0.953)	0.925	0.658	0.756	0.884
	XGB + LSM	0.821 (0.755-0.880)	0.970 (0.950-0.990)	0.954	0.739	0.81	0.908	< 0.001
	APRI	0.710 (0.646-0.769)	0.737 (0.669-0.805)	0.917	0.329	0.715	0.684	< 0.001
	FIB-4	0.688 (0.622-0.748)	0.752 (0.687-0.816)	0.912	0.165	0.681	0.765	< 0.001

LSM: Liver stiffness measurement; APRI: Aspartate aminotransferase to platelet ratio index; FIB-4: Fibrosis index based on the 4 factors; XGBoost: Extreme Gradient Boosting.