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

Table 2 Diagnostic performance of the machine learning models for moderate to advanced liver fibrosis

Cohort	Models	Accuracy	Area under receiver operating characteristic curve	F1 scores	Sensitivity	Specificity	Positive predictive value	Negative predictive value	P value
Training	XGB	0.862 (0.830-0.891)	0.934 (0.914-0.955)	0.838	0.958	0.744	0.856	0.876
	RF	0.877 (0.846-0.904)	0.857 (0.825-0.888)	0.817	0.944	0.72	0.869	0.894	< 0.001
	SVM	0.8199 (0.784-0.852)	0.773 (0.738-0.809)	0.724	0.969	0.578	0.789	0.92	< 0.001
	LR	0.784 (0.746-0.818)	0.805 (0.764-0.846)	0.715	0.901	0.593	0.782	0.787	< 0.001
	NB	0.385 (0.343-0.428)	0.503 (0.499-0.575)	0.012	0.006	1	1	0.383	< 0.001
Validation	XGB	0.853 (0.799-0.896)	0.917 (0.880-0.953)	0.78	0.959	0.658	0.837	0.897
	RF	0.875 (0.824-0.915)	0.840 (0.788-0.892)	0.758	0.959	0.626	0.863	0.905	< 0.001
	SVM	0.813 (0.755-0.861)	0.740 (0.684-0.796)	0.658	0.986	0.494	0.784	0.951	< 0.001
	LR	0.790 (0.731-0.842)	0.745 (0.669-0.821)	0.641	0.959	0.481	0.772	0.864	< 0.001
	NB	0.357 (0.294-0.424)	0.503 (0.495-0.510)	0.014	0.007	1	1	0.354	< 0.001

LR: Logistic regression; RF: Random forest; SVM: Support Vector Machine; NB: Naive Bayes; XGBoost: Extreme Gradient Boosting.