What question did this study set out to answer?

The study aims to create and validate a machine learning model for predicting the progression of type 2 diabetic kidney disease using routine clinical parameters.

June 1, 2026Open Access

Development and validation of an explainable neural network model for predicting progression in type 2 diabetic kidney disease

Key Points

The study aims to create and validate a machine learning model for predicting the progression of type 2 diabetic kidney disease using routine clinical parameters.
Conducted a single-center retrospective cohort study in Quzhou, China, enrolling 349 patients with T2DKD from June 2022 to June 2025.
Identified four core predictors using LASSO regression and multivariable logistic regression from 36 clinical characteristics.
Evaluated machine learning model performance using discrimination, calibration, and clinical net benefit metrics.
The neural network model demonstrated an area under the curve (AUC) of 0.742 in the test dataset.
Achieved satisfactory calibration with a Hosmer–Lemeshow P-value of 0.2020 and the lowest Brier score of 0.2105.
Confirmed stable feature importance rankings via SHAP analysis with a Pearson correlation of r = 0.976.

Abstract

Background Type 2 diabetic kidney disease (T2DKD) affects 20–40% of patients with type 2 diabetes mellitus and has become the leading cause of end-stage renal disease globally. Early identification of patients at risk of rapid progression remains challenging, as existing prediction models often rely on complex indicators unsuitable for primary care settings. This study aimed to develop and validate a machine learning model using routine clinical parameters to predict T2DKD progression. Methods This single-center retrospective cohort study enrolled 349 patients diagnosed with T2DKD according to clinical criteria at Quzhou People’s Hospital in China between June 2022 and June 2025. From 36 baseline characteristics, four core predictors were identified through least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression. Six machine learning models were constructed, and model performance was evaluated by discrimination, calibration, and clinical net benefit. Model interpretability was assessed using SHapley Additive exPlanations (SHAP) analysis. Results Metabolic dysfunction-associated steatotic liver disease (MASLD), aspartate aminotransferase (AST), diabetic peripheral neuropathy (DPN), and age were identified as independent core predictors. The neural network (NN) model achieved optimal performance in the test dataset, with an area under the curve (AUC) of 0.742, satisfactory calibration (Hosmer–Lemeshow P = 0.2020), the lowest Brier score (0.2105), and superior clinical net benefit across risk thresholds of 0.2–0.6. SHAP analysis confirmed stable feature importance rankings between training and test datasets (Pearson r = 0.976) and revealed synergistic interactions among MASLD, AST, and DPN. Conclusion A NN model incorporating four routine clinical indicators effectively predicts T2DKD progression risk. This cost-effective tool is suitable for clinical practice and community health services, offering a scalable solution for early intervention and prognosis improvement.

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