Development and prospective validation of a machine learning model to predict mortality in cirrhosis with esophageal variceal bleeding

Table 3 Performance metrics for the random forest model in internal and prospective validation, mean ± SD

Metric	Internal validation	Prospective validation
AUC	0.715 ± 0.106	0.927 ± 0.053
Accuracy	0.688 ± 0.089	0.829 ± 0.078
Recall	0.752 ± 0.127	0.867 ± 0.126
F1 score	0.657 ± 0.109	0.760 ± 0.120
Brier score	0.218 ± 0.038	0.175 ± 0.016

Internal validation metrics were derived from bootstrap resampling (1000 iterations). Prospective validation was performed on an independent cohort (n = 24). AUC: Area under the curve.