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已发表论文
基于机器学习的 2 型糖尿病老年患者周围神经病变风险预测模型的开发与验证
Authors Peng J , Xue D, Li J, Wei L, Wang Y
Received 11 October 2025
Accepted for publication 28 December 2025
Published 8 January 2026 Volume 2026:19 573535
DOI https://doi.org/10.2147/RMHP.S573535
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Gulsum Kaya
Jinling Peng,1,2 Dandan Xue,2 Juanjuan Li,2 Lihua Wei,1 Yanmei Wang2
1School of Medicine, Shihezi University, Shihezi, Xinjiang, 832000, People’s Republic of China; 2Department of Nursing, Gongli Hospital of Shanghai Pudong New Area, Shanghai, 200135, People’s Republic of China
Correspondence: Yanmei Wang, Department of Nursing, Gongli Hospital of Shanghai Pudong New Area, Miao Pu Road, Shanghai, 200135, People’s Republic of China, Tel +86 18721159503, Email 877927981@qq.com
Background: Diabetic peripheral neuropathy (DPN) is highly prevalent among elderly patients with type 2 diabetes; however, existing models exhibit suboptimal performance and lack specificity. This study aims to develop and validate a machine learning-based model for early identification of DPN risk.
Methods: We retrospectively collected the data of 1450 elderly patients with type 2 diabetes using the electronic medical record system of the National Metabolic Management Center (MMC) at a tertiary hospital in Shanghai’s Pudong New Area from March 2022 to March 2025. The dataset included general information, disease-related indicators, and laboratory results. We randomly divided the dataset into training and testing sets in a 7:3 ratio. After feature preprocessing and selection, four machine learning algorithms—logistic regression, naïve Bayes, random forest, and extreme gradient boosting (XGBoost)—were used to construct prediction models. Hyperparameter tuning was executed through grid search combined with 5-fold cross-validation, and model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, precision, recall, F1-score, calibration curves, and Decision Curve Analysis (DCA). The SHapley Additive exPlanations (SHAP) analysis was applied for model interpretation.
Results: The prevalence of DPN was 42.9% (623/1450). Nine variables were identified as independent predictors: diabetes duration, HbA1c, sleep quality, Charlson Comorbidity Index, sugar-sweetened beverage intake, peripheral arterial disease, sedentary behavior, smoking, and hypertension. Among the models, XGBoost performed best with an AUC of 0.951, accuracy of 0.878, precision of 0.876, recall of 0.834, F1-score of 0.855, and Brier score of 0.087. SHAP analysis confirmed the dominant contribution of diabetes duration and HbA1c to model predictions.
Conclusion: The XGBoost-based risk prediction model exhibited robust predictive performance and clinical utility for DPN in elderly patients with type 2 diabetes, offering potential for early identification of high-risk individuals and guiding targeted clinical interventions.
Keywords: machine learning, elderly, type 2 diabetes, diabetic peripheral neuropathies, predictive model
1School of Medicine, Shihezi University, Shihezi, Xinjiang, 832000, People’s Republic of China; 2Department of Nursing, Gongli Hospital of Shanghai Pudong New Area, Shanghai, 200135, People’s Republic of China
Correspondence: Yanmei Wang, Department of Nursing, Gongli Hospital of Shanghai Pudong New Area, Miao Pu Road, Shanghai, 200135, People’s Republic of China, Tel +86 18721159503, Email 877927981@qq.com
Background: Diabetic peripheral neuropathy (DPN) is highly prevalent among elderly patients with type 2 diabetes; however, existing models exhibit suboptimal performance and lack specificity. This study aims to develop and validate a machine learning-based model for early identification of DPN risk.
Methods: We retrospectively collected the data of 1450 elderly patients with type 2 diabetes using the electronic medical record system of the National Metabolic Management Center (MMC) at a tertiary hospital in Shanghai’s Pudong New Area from March 2022 to March 2025. The dataset included general information, disease-related indicators, and laboratory results. We randomly divided the dataset into training and testing sets in a 7:3 ratio. After feature preprocessing and selection, four machine learning algorithms—logistic regression, naïve Bayes, random forest, and extreme gradient boosting (XGBoost)—were used to construct prediction models. Hyperparameter tuning was executed through grid search combined with 5-fold cross-validation, and model performance was evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), accuracy, precision, recall, F1-score, calibration curves, and Decision Curve Analysis (DCA). The SHapley Additive exPlanations (SHAP) analysis was applied for model interpretation.
Results: The prevalence of DPN was 42.9% (623/1450). Nine variables were identified as independent predictors: diabetes duration, HbA1c, sleep quality, Charlson Comorbidity Index, sugar-sweetened beverage intake, peripheral arterial disease, sedentary behavior, smoking, and hypertension. Among the models, XGBoost performed best with an AUC of 0.951, accuracy of 0.878, precision of 0.876, recall of 0.834, F1-score of 0.855, and Brier score of 0.087. SHAP analysis confirmed the dominant contribution of diabetes duration and HbA1c to model predictions.
Conclusion: The XGBoost-based risk prediction model exhibited robust predictive performance and clinical utility for DPN in elderly patients with type 2 diabetes, offering potential for early identification of high-risk individuals and guiding targeted clinical interventions.
Keywords: machine learning, elderly, type 2 diabetes, diabetic peripheral neuropathies, predictive model