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Updated: Jun 7, 2025
Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
Published on: November 29, 2024
Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in
Zouxi Du1,2,3, Xiaoning Liu4, Jiayu Li2
1The First School of Clinical Medicine, Lanzhou University, Lanzhou, Gansu, China.
Aims:
Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk of DN among individuals with T1DM.
Methods:
This was a retrospective, multicenter study conducted across 19 hospitals in Gansu Province, China (No: 2022-473). In total, 1368 patients were eligible for analysis among 1633 collected T1DM patients from January 2016 to December 2023. Recursive feature elimination using random forest and fivefold cross-validation was conducted to identify key features. Among the 12 initial ML algorithms, the optimal ML model was developed and validated externally in a distinct population, and its predictive outcomes were explained via the SHapley additive exPlanations method, which offered personalized decision insights.
Results:
Among the 1368 T1DM patients, 324 had DN. The extreme gradient boosting (XGBoost) model, which achieved optimal performance with an AUC of 83% (95% confidence interval [CI]: 76‒89), was selected to predict the risk of DN among T1DM patients. The DN predictive model included variables such as T1DM duration, postprandial glucose (PPG), systolic blood pressure (SBP), glycated hemoglobin (HbA1c), serum creatinine (Scr) and low-density lipoprotein cholesterol (LDL-C). External validation confirmed the reliability of the model, with an AUC of 76% (95% CI: 70‒82).
Conclusions:
The ML prediction tool has potential for advancing early and precise identification of the risk of DN among T1DM patients. Although successful external validation indicated that the developed model can provide a promising strategy for clinical adoption and help improve patient outcomes through timely and accurate risk assessment, additional prospective data and further validation in diverse populations are necessary.
