ORIGINAL RESEARCH article
Front. Endocrinol.
Sec. Clinical Diabetes
Volume 16 - 2025 | doi: 10.3389/fendo.2025.1614657
This article is part of the Research TopicDiabetes Complications: Navigating Challenges and Unveiling New SolutionsView all articles
Machine Learning-Based Prediction of Diabetic Peripheral Neuropathy: Model Development and Clinical Validation
Provisionally accepted
- 1Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, China
- 2Department of Nursing, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
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Background: Diabetic peripheral neuropathy (DPN) is a common and debilitating complication of type 2 diabetes mellitus (T2DM), significantly impacting patients' quality of life and increasing healthcare burdens. Early prediction and intervention are critical to mitigating its impact.Methods: This study analyzed 1,544 diabetic patients from the First Affiliated Hospital of Shandong First Medical University, who were randomly divided into a training cohort (n = 1,082) and a testing cohort (n = 462) using a 7:3 split ratio. Feature selection was performed using both Boruta and LASSO algorithms, and the intersection of the selected variables was used as the final predictor set. Eight key predictors were identified from 23 variables, including diabetes duration, uric acid, HbA1c, NLR, smoking status, SCR, LDH, and hypertension. Nine machine learning models were developed and compared for DPN risk prediction.Results: Stochastic Gradient Boosting (SGBT) demonstrated the best performance (training AUC: 0.933, 95% CI: 0.921-0.946; testing AUC: 0.811, 95% CI: 0.776-0.843). Shapley Additive Explanations (SHAP) analysis provided interpretability, highlighting the clinical importance of diabetes duration and HbA1c among other predictors.This study establishes a robust predictive tool for early DPN detection, laying the foundation for improved prevention and management strategies.
Keywords: Diabetic peripheral neuropathy, machine learning, Interpretable, Clinical data, Risk prediction model
Received: 19 Apr 2025; Accepted: 20 May 2025.
Copyright: © 2025 Sun, Sun, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Fei Wang, Department of Nursing, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, China
Li Liu, Department of Neurosurgery, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital, Jinan, 250014, Shandong Province, China
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