Development and internal validation of an interpretable risk prediction model for diabetic peripheral neuropathy in type 2 diabetes: a single-centre retrospective cohort study in China

作者：Lianhua Liu, Bo Bi, Mei Gui, Linli Zhang, Feng Ju, Xiaodan Wang, Li Juan Cao · 发表于：BMJ Open · 年份：2025 · DOI：10.1136/bmjopen-2024-092463 · 被引用次数：9 · 研究领域：Diabetic Foot Ulcer Assessment and Management、Pain Mechanisms and Treatments、Medical Imaging and Analysis

OBJECTIVE: Diabetic peripheral neuropathy (DPN) is a common and serious complication of diabetes, which can lead to foot deformity, ulceration, and even amputation. Early identification is crucial, as more than half of DPN patients are asymptomatic in the early stage. This study aimed to develop and validate multiple risk prediction models for DPN in patients with type 2 diabetes mellitus (T2DM) and to apply the Shapley Additive Explanation (SHAP) method to interpret the best-performing model and identify key risk factors for DPN. DESIGN: A single-centre retrospective cohort study. SETTING: The study was conducted at a tertiary teaching hospital in Hainan. PARTICIPANTS AND METHODS: Data were retrospectively collected from the electronic medical records of patients with diabetes admitted between 1 January 2021 and 28 March 2023. After data preprocessing, 73 variables were retained for baseline analysis. Feature selection was performed using univariate analysis combined with recursive feature elimination (RFE). The dataset was split into training and test sets in an 8:2 ratio, with the training set balanced via the Synthetic Minority Over-sampling Technique. Six machine learning algorithms were applied to develop prediction models for DPN. Hyperparameters were optimised using grid search with 10-fold cross-validation. Model performance was assessed using various metrics on the test set, and the SHAP method was used to interpret the best-performing model. RESULTS: The study incl...