Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence

Figure 4 Performance evaluation of the logistic regression model across the training, validation, and external test sets. A: Receiver operating characteristic (ROC) curve and area under the curve (AUC) value for the training set; B: ROC curve and AUC values for the validation set, constructed through random selection of 30% of training cases with 5-fold cross-validation. Five solid lines represent individual validation fold outcomes; C: ROC curve and AUC value for the independent external test set; D: Learning curves show performance progression, with training and validation sets represented by red and blue dashed lines, respectively. ROC: Receiver operating characteristic; CI: Confidence interval.