Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study

Figure 1 Flowchart of study procedure. SHAP: SHapley Additive exPlanations.

Figure 2 Interaction between variables in a correlation matrix. PrCa: Prstoperative calcium ion concentration; PrCRE: Preoperative serum creatinine concentration; PoCa: Postoperative calcium ion concentration; rLNs: Positive lymph node count; PrPLT: Preoperative platelet concentration.

Figure 3 Testing and evaluation of eXtreme Gradient Boosting model based on raw data. A: Area under the receiver operating characteristic curve comparison between models; B: The confusion matrix of eXtreme Gradient Boosting model; C: Comparison of decision curve analysis curves between models; D: Comparison of calibration curves between models. AUC: Area under the receiver operating characteristic curve; ROC: Receiver operating characteristic; SVM: Support vector machine; GBM: Gradient boosting machines; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine.

Figure 4 Testing and evaluation of eXtreme Gradient Boosting model based on SMOTE-resampled data. A: Area under the receiver operating characteristic curve comparison between models; B: The confusion matrix of eXtreme Gradient Boosting model; C: Comparison of decision curve analysis curves between models; D: Comparison of calibration curves between models. AUC: Area under the receiver operating characteristic curve; ROC: Receiver operating characteristic; SVM: Support vector machine; GBM: Gradient boosting machines; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine.

Figure 5 Receiver operating characteristic curve analysis of the eXtreme Gradient Boosting model based on the external validation dataset. AUC: Area under the receiver operating characteristic curve; ROC: Receiver operating characteristic; SVM: Support vector machine; GBM: Gradient boosting machines; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine.

Figure 6 Interpretation of the eXtreme Gradient Boosting model using SHapley Additive exPlanations. A: Mean importance ranking of features displayed by SHapley Additive exPlanations (SHAP); B: Bee colony diagram of characterization attributes in SHAP. SHAP: SHapley Additive exPlanations.

Figure 7 Interpretation of the light gradient boosting machine model using SHapley Additive exPlanations. A: A patient who did not develop anastomotic leakage; B: A patient who developed anastomotic leakage. SHAP: SHapley Additive exPlanations; PoCa: Postoperative calcium ion concentration; rLNs: Positive lymph node count; PrPLT: Preoperative platelet concentration.

Figure 8 Interaction of significant factors based on SHapley Additive exPlanations. SHAP: SHapley Additive exPlanations; PrCa: Preoperative calcium ion concentration; PrCRE: Preoperative serum creatinine concentration; PoCA: Postoperative calcium ion concentration; rLNs: Positive lymph node count; PrPLT: Preoperative platelet concentration.