AUTHOR=Liu Haochen , Li Xiaomiao , Shi Ke , Lei Fengyu , Wang Ziyan , Gao Ziyuan , Liu Yunxi , Zhu Jing , Zhai Jiajia , Zhang Yi , Li Xinyu , Wang Shiyu , Niu Yu , Ma Louyan , Zhang Tianxiao 

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

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1657366

DOI=10.3389/fendo.2025.1657366

ISSN=1664-2392

ABSTRACT=ObjectiveTo 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 methodsData 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.ResultsThe analysis identified 33 risk factors, including 6 shared risk factors (UACR for DN and DR; diabetes duration for DR, DPN, and DF; IBILI for DF and DPN; history of DN for DR and DF; U-Cr for DR and DF; MCHC for DN and DPN) and 27 unique risk factors. Model performance was robust: for DN, TabPFN achieved an AUC of 0.905 and Random Forest an accuracy of 0.878; for DR, LightGBM attained an AUC of 0.794; for DPN, both TabPFN and CatBoost achieved a perfect recall of 1.000 and F1-score of 0.915; and for DF, LightGBM attaining the highest AUC of 0.704. SHAP analysis highlighted key features for each complication, such as UACR and Y-protein for DN, diabetes duration and TPOAB for DR, history of DN and IBILI for DF, and diabetes duration and SBP for DPN.ConclusionThis study employed interpretable machine learning to characterize risk factor profiles for multiple T2DM complications, identifying both common and distinct factors associated with major complications. The findings provide a foundation for exploring personalized risk management strategies and highlight the potential of data-driven approaches to inform early intervention research in T2DM complications.