Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery

Figure 7 SHapley Additive exPlanations dependence plot of the categorical boosting model. Each panel shows that each feature affects the output of the final model. A: Operative time; B: Postoperative serum creatinine; C: Postoperative white blood cell count; D: Intraoperative red blood cell transfusion; E: Postoperative bilirubin; F: Postoperative prothrombin time; G: Postoperative activated partial thromboplastin time; H: Total input on the day of surgery; I: Postoperative albumin. The X-axis represents the raw values of each feature, and the Y-axis indicates the SHapley Additive exPlanations (SHAP) values of the features. When the SHAP value of a specific feature exceeds zero, it indicates an increased risk of acute kidney injury. A footnote of 0 (such as variable 0) means the preoperative value while a footprint of 1 (such as variable 1) means the postoperative value. SHAP: SHapley Additive exPlanations; SCr: Serum creatinine; WBC: White blood cell; RBC: Red blood cell; TBil: Total bilirubin; PT: Prothrombin time; APTT: Activated partial thromboplastin time; Alb: Albumin; POD: Postoperative day.