Prediction of pancreatic fistula after pancreatoduodenectomy using machine learning

Objective: to analyze the results of pancreatoduodenectomy (PD) and identify predictive risk factors for postoperative pancreatic fistula (PF) using machine learning (ML) technology. Material and Methods. A non-randomized study of treatment outcomes in 128 patients, who underwent PD for periampullary carcinoma between 2018 and 2023, was conducted. To predict PF, the ML models based on the multilayer perceptron and binary logistic regression (BLR) in SPSS Statistics v.26, were used. The Receiver Operator Characteristics (ROC) analysis was used to assess the accuracy of the models. To compare ROC curves, the DeLong test was used.