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.

Figure 2 Evaluation of the random forest model in the test cohort. A: Receiver operating characteristic curves and area under the curve values for the testing set; B: Confusion matrix for the random forest (RF) model applied to the testing set; C: Comparison of predicted probabilities by the RF model for patients with and without parastomal hernia in the test cohort; D: Decision curve analysis for the test group; E: Calibration curve of the testing set. ^aP < 0.001 vs control group. AUC: Area under the curve.

Figure 3 SHapley Additive exPlanation analysis of the random forest model. A: Average absolute SHapley Additive exPlanation (SHAP) values for various clinical features; B: SHAP values illustrating the influence of different clinical features on the model's predictions. SHAP: SHapley Additive exPlanation; BMI: Body mass index.