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

Figure 5 Calibration plots for mortality prediction models. A: Random Forest; B: Model for end-stage liver disease-serum sodium; C: Model for end-stage liver disease; D: Child-Pugh; E: Glasgow-Blatchford scores. Calibration curves (solid lines) compare predicted probabilities (X-axis) with observed mortality rates (Y-axis). Perfect calibration is shown by the diagonal dashed line. Histograms display the distribution of predictions. The random forest model shows excellent calibration (Brier score = 0.067), while traditional scores show systematically poorer calibration (Brier scores 0.199-0.247). n = 97 patients from the retrospective cohort. MELD: Model for end-stage liver disease; MELD-Na: Model for end-stage liver disease-serum sodium.