Early prediction of severe acute pancreatitis based on improved machine learning models

Abstract

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models, and to analyze its clinical value. Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease, the patients were divided into the severe group (n=88) and the non-severe group (n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features. Results In the training set, the area under curve (AUC) of the improved machine learning model was 0.922.In the testing set, the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein, blood chlorine, blood magnesium and fibrinogen level, which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis. Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.

Keywords

• severe acute pancreatitis
• machine learning models
• archimedes optimization algorithm
• c-reactive protein
• magnesium