Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models

Table 5 Comparison of machine learning algorithms for predicting significant hepatic fibrosis

Parameter	Model	AUC (95%CI)	Sensitivity	Specificity	PPV	NPV	F1 score	Brier score
Training	Random forest	0.921 (0.889-0.953)	0.825	0.900	0.892	0.837	0.857	0.141
	AdaBoost	0.881 (0.842-0.921)	0.800	0.775	0.780	0.795	0.790	0.157
	SVM	0.791 (0.742-0.842)	0.675	0.850	0.818	0.723	0.740	0.188
	Logistic regression	0.750 (0.698-0.803)	0.625	0.825	0.781	0.688	0.694	0.198
	Naive Bayes	0.751 (0.700-0.806)	0.925	0.525	0.661	0.875	0.771	0.217
	KNN	0.658 (0.602-0.716)	0.850	0.450	0.607	0.750	0.708	0.227
Validation	Random forest	0.905 (0.870-0.940	0.800	0.880	0.870	0.820	0.835	0.152
	AdaBoost	0.860 (0.820-0.903)	0.770	0.750	0.760	0.765	0.765	0.169
	SVM	0.760 (0.710-0.812)	0.650	0.820	0.790	0.700	0.710	0.205
	Logistic regression	0.735 (0.680-0.788)	0.600	0.800	0.760	0.660	0.675	0.210
	Naive Bayes	0.740 (0.690-0.795)	0.900	0.500	0.640	0.840	0.745	0.230
	KNN	0.630 (0.575-0.690)	0.820	0.420	0.580	0.720	0.685	0.245

AUC: Area under the curve; CI: Confidence interval; SVM: Support vector machine; KNN: K-nearest neighbors; NPV: Negative predictive value; PPV: Positive predictive value.