Explainable artificial intelligence model predicting the risk of all-cause mortality in patients with type 2 diabetes mellitus.

ABSTRACT:

Background

Type 2 diabetes mellitus (T2DM) is a highly prevalent non-communicable chronic disease that substantially reduces life expectancy. Accurate estimation of all-cause mortality risk in T2DM patients is crucial for personalizing and optimizing treatment strategies.

Methods

This study analyzed a cohort of 554 patients (aged 40-87 years) with diagnosed T2DM over a maximum follow-up period of 16.8 years, during which 202 patients (36%) died. Key survival-associated features were identified, and multiple machine learning (ML) models were trained and validated to predict all-cause mortality risk. To improve model interpretability, Shapley additive explanations (SHAP) was applied to the best-performing model.

Results

The extra survival trees (EST) model, incorporatin

SUBMITTER: Vershinina O 

PROVIDER: S-EPMC12575121 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature