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已发表论文
基于机器学习算法的中国社区 2 型糖尿病及其并发症健康管理的回顾性研究
Authors Luo X, Liang J, Pan H, Zhou D, Ye H, Zhao Y, Sun J, Zhang A
Received 5 August 2025
Accepted for publication 10 November 2025
Published 27 November 2025 Volume 2025:18 Pages 4367—4384
DOI https://doi.org/10.2147/DMSO.S556130
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Prof. Dr. Ernesto Maddaloni
Xin Luo,1,* Jingming Liang,1,* Hong Pan,1 Dian Zhou,1 Hong Ye,2 Ying Zhao,2 Jijia Sun,2 An Zhang1
1Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Department of Mathematical Sciences and Computational Intelligence, School of Traditional Chinese Medicine and Artificial Intelligence, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jijia Sun, Department of Mathematical Sciences and Computational Intelligence, School of Traditional Chinese Medicine and Artificial Intelligence, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai, 201203, People’s Republic of China, Tel +86-021-51322185, Email jijiasun@163.com An Zhang, Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai, 201203, People’s Republic of China, Tel +86-13651673015, Email 13052289046@163.com
Background: The global burden of diabetes mellitus (DM) and its complications is a major global public health challenge. This study aimed to improve community capacity for DM management by developing a risk prediction model for complications and providing health management recommendations using machine learning (ML).
Methods: A retrospective analysis was conducted of 4916 type 2 diabetes (T2DM) patients from Shanghai communities. Model I was developed and compared by using the least absolute shrinkage and selection operator (Lasso) regression, support vector machine (SVM), decision tree (DT) and logistic regression (LR). A Bayesian Network (BN) model to uncover potential causal relationships. Model I was evaluated and adjusted using the receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). The BN model was assessed using AUC, accuracy, specificity, and sensitivity.
Results: Five consistent predictors were identified: disease course, diastolic blood pressure, HbA1c, urinary creatinine, and urinary microalbumin. Model I achieved AUCs of 0.695 (training) and 0.676 (validation), with decision curve analysis showing risk thresholds of 12– 92% and 20– 92% respectively. The calibration curves showed good calibration. The tree-augmented BN model achieved the AUC of 0.755, accuracy of 0.733, specificity of 0.802 and sensitivity of 0.519.
Conclusion: Effective models for predicting complication risk in T2DM patients were developed. T2DM patients with chronic comorbidities, higher income, and longer disease duration as key targets for community management. We recommend prioritizing UMA as a key monitoring indicator and strengthening comprehensive interventions, including health education, dietary self-management, and family-community support.
Keywords: T2DM, machine learning, health management
1Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Department of Mathematical Sciences and Computational Intelligence, School of Traditional Chinese Medicine and Artificial Intelligence, Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Jijia Sun, Department of Mathematical Sciences and Computational Intelligence, School of Traditional Chinese Medicine and Artificial Intelligence, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai, 201203, People’s Republic of China, Tel +86-021-51322185, Email jijiasun@163.com An Zhang, Department of Health Management, School of Public Health, Shanghai University of Traditional Chinese Medicine, No. 1200 Cailun Road, Pudong New Area, Shanghai, 201203, People’s Republic of China, Tel +86-13651673015, Email 13052289046@163.com
Background: The global burden of diabetes mellitus (DM) and its complications is a major global public health challenge. This study aimed to improve community capacity for DM management by developing a risk prediction model for complications and providing health management recommendations using machine learning (ML).
Methods: A retrospective analysis was conducted of 4916 type 2 diabetes (T2DM) patients from Shanghai communities. Model I was developed and compared by using the least absolute shrinkage and selection operator (Lasso) regression, support vector machine (SVM), decision tree (DT) and logistic regression (LR). A Bayesian Network (BN) model to uncover potential causal relationships. Model I was evaluated and adjusted using the receiver operating characteristic (ROC) curve, area under the curve (AUC), calibration curve, and decision curve analysis (DCA). The BN model was assessed using AUC, accuracy, specificity, and sensitivity.
Results: Five consistent predictors were identified: disease course, diastolic blood pressure, HbA1c, urinary creatinine, and urinary microalbumin. Model I achieved AUCs of 0.695 (training) and 0.676 (validation), with decision curve analysis showing risk thresholds of 12– 92% and 20– 92% respectively. The calibration curves showed good calibration. The tree-augmented BN model achieved the AUC of 0.755, accuracy of 0.733, specificity of 0.802 and sensitivity of 0.519.
Conclusion: Effective models for predicting complication risk in T2DM patients were developed. T2DM patients with chronic comorbidities, higher income, and longer disease duration as key targets for community management. We recommend prioritizing UMA as a key monitoring indicator and strengthening comprehensive interventions, including health education, dietary self-management, and family-community support.
Keywords: T2DM, machine learning, health management