Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.

ABSTRACT:

Background

Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors.

Methods

This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. F

SUBMITTER: Li X 

PROVIDER: S-EPMC11539846 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature