Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.

ABSTRACT:

Objective

To develop a self-reportable risk assessment tool for elderly type 2 diabetes mellitus (T2DM) patients, evaluating risks of diabetic nephropathy (DN), retinopathy (DR), peripheral neuropathy (DPN), and diabetic foot (DF) using machine learning, thereby providing new insights and tools for the screening and intervention of these complications.

Materials and methods

Data from 1,448 T2DM patients at Xi'an No.9 Hospital were used. After preprocessing, five machine learning algorithms (XGBoost, LightGBM, Random Forest, TabPFN, CatBoost) were applied. Models were trained on 70% of the data and evaluated on 30%, with performance assessed by multiple metrics and SHAP analysis for feature importance.

Results

The analysis identified 33 risk factors, including 6 shared

SUBMITTER: Liu H 

PROVIDER: S-EPMC12457174 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature