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

Figure 2 Feature selection by Boruta algorithm and least absolute shrinkage and selection operator. A: Through Boruta the 18 filtered variables were as follows: Surgery duration; Intensive care unit admission after surgery; Surgical approach; Intraoperative red blood cell (RBC) transfusion; Tumor length; Postoperative serum creatinine; Postoperative albumin; Intraoperative blood loss; Sex; Postoperative prothrombin time; Age; Postoperative bilirubin; Postoperative activated partial thromboplastin time; Preoperative prothrombin time; Malignancy history; History of stroke; Total input on postoperative day 0; And angiotensin-converting enzyme inhibitor/angiotensin receptor blocker medication; B: Through least absolute shrinkage and selection operator, the 11 filtered variables were as follows: Intensive care unit admission after surgery; Nonsteroidal anti-inflammatory drugs; Malignancy history; History of stroke; Surgery duration; Postoperative white blood cell; Surgical approach; Postoperative serum creatinine; Postoperative bilirubin; Preoperative bilirubin; And intraoperative RBC transfusion. A footnote of 0 (such as variable 0) indicates the preoperative value while a footprint of 1 (such as variable 1) indicates the postoperative value. CVM: Cross-validation mean; Alb: Albumin; APTT: Activated partial thromboplastin time; TBil: Total bilirubin; PT: Prothrombin time; RBC: Red blood cell; ICU: Intensive care unit; SCr: Serum creatinine.