Development and validation of machine learning models for esophagogastric variceal bleeding risk in hepatocellular carcinoma patients

Table 3 Performance evaluation metrics of six machine learning models on the validation set

Model	Brier	Accuracy	Precision	Sensitivity	Specificity	F1 score	Youden’s index	Cut-off
DT	0.114	0.875	0.857	0.889	0.862	0.873	0.751	0.200
RF	0.106	0.893	0.920	0.852	0.931	0.885	0.783	0.590
XGBoost	0.112	0.875	0.917	0.815	0.931	0.863	0.746	0.590
LightGBM	0.136	0.839	0.909	0.741	0.931	0.816	0.672	0.670
SVM	0.105	0.893	0.889	0.889	0.897	0.889	0.785	0.550
ANN	0.104	0.893	0.957	0.815	0.966	0.880	0.780	0.650

DT: Decision tree; RF: Random forest; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine; SVM: Support vector machine; ANN: Artificial neural network.