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

TL;DR

This study developed and validated an interpretable machine learning model using clinical features to accurately predict long-term all-cause mortality risk in patients with type 2 diabetes, aiding personalized treatment decisions.

Contribution

It introduces a novel, highly predictive and interpretable machine learning model for mortality risk in T2DM patients, with detailed feature analysis using SHAP for clinical applicability.

Findings

EST model achieved a C-statistic of 0.776.

Model predictions had AUCs of 0.86, 0.80, 0.84, and 0.83 for 5-, 10-, 15-, and 16.8-year predictions.

SHAP explanations provided insights into individual decision processes.

Abstract

Objective. 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. Research Design and 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, incorporating ten key features, demonstrated the best predictive performance. The model achieved a C-statistic of 0.776, with the area under the receiver operating characteristic curve (AUC) values of 0.86, 0.80, 0.841, and 0.826 for 5-, 10-, 15-, and 16.8-year all-cause mortality predictions, respectively. The SHAP approach was employed to interpret the model's individual decision-making processes. Conclusions. The developed model exhibited strong predictive performance for mortality risk assessment. Its clinically interpretable outputs enable potential bedside application, improving the identification of high-risk patients and supporting timely treatment optimization.