Machine learning-based prediction models for liver-related events in patients with hepatitis B-related cirrhosis and clinically significant portal hypertension

Table 2 Performances of the machine learning models for predicting liver related events

Model	Accuracy	Recall	F1 score	Sensitivity	Specificity	PPV	NPV	AUC	CV-AUC
SVM	0.775	0.789	0.785	0.789	0.759	0.780	0.768	0.834	0.787 ± 0.060
RF	0.951	0.950	0.953	0.950	0.952	0.955	0.946	0.975	0.842 ± 0.045
LR	0.731	0.739	0.741	0.739	0.723	0.743	0.719	0.790	0.740 ± 0.055
XGBoost	0.951	0.950	0.953	0.950	0.952	0.955	0.946	0.965	0.830 ± 0.061
KNN	0.769	0.756	0.773	0.756	0.783	0.791	0.747	0.833	0.737 ± 0.045

PPV: Positive predictive value; NPV: Negative predictive value; AUC: Area under the receiver operating characteristic curve; CV: Cross-validation; SVM: Support vector machine; RF: Random forest; LR: Logistic regression; XGBoost: Extreme gradient boosting; KNN: K-nearest neighbor.