Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning

Figure 1 Features selection and model performance in the training cohort. A: Relevant features identified by the Boruta algorithm; B: Receiver operating characteristic curves and area under the curve values for the five models; C: Comparison of performance metrics across all five models; D: Confusion matrix of the random forest (RF) model; E: Comparison of predicted probabilities from the RF model for patients with and without parastomal hernia in the training cohort. ^aP < 0.001 vs control group. SVM: Support vector machine; LR: Logistic regression; KNN: K-nearest neighbors; RF: Random forest; XGBoost: EXtreme gradient boosting; ASA: American Society of Anesthesiologists.