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已发表论文
利用可解释机器学习识别影响社区居住的 2 型糖尿病老年患者自我同情的最关键因素
Authors Xu J , Yang J, Lu Y, Yang J, Gu C, Zhu J, Yang L
Received 29 July 2025
Accepted for publication 23 November 2025
Published 15 December 2025 Volume 2025:18 Pages 4571—4586
DOI https://doi.org/10.2147/DMSO.S556917
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Professor Melissa Olfert
Junxian Xu,1 Jianzhong Yang,2 Yuping Lu,3 Jieyu Yang,2 Chao Gu,4 Jiahuan Zhu,2 Lanni Yang2
1College of Medicine, Jiaxing University, Jiaxing, People’s Republic of China; 2Community Healthcare Center of Chengnan Sub-District, Jiaxing, People’s Republic of China; 3Community Healthcare Center of Jiabei Sub-District, Jiaxing, People’s Republic of China; 4Affiliated Hospital of Jiaxing University, Jiaxing, People’s Republic of China
Correspondence: Junxian Xu, College of Medicine, Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China, Tel +86-13511357431, Email xujunxian_2022@163.com
Objective: Managing diabetes daily can be an emotional burden for older adults. Research shows that self-compassion, which refers to the ability to be kind and understanding toward oneself, can help improve emotional well-being. This study aimed to develop a machine learning prediction model to identify the influencing factors of self-compassion among community-dwelling older adults with type 2 diabetes.
Methods: We conducted this study in Jiaxing, China, during July and August 2024. We invited community-dwelling older adults with type 2 diabetes to complete a questionnaire that measured their levels of self-compassion, depression, and anxiety. Our goal was to find which of 26 different personal and health-related factors most influenced self-compassion. To achieve this, we used several machine learning algorithms to build and compare predictive models, selecting the best-performing one. Finally, we applied a technique called SHapley Additive exPlanations (SHAP) to clearly understand and interpret how each factor impacts self-compassion.
Results: The random forest model performed the best. SHAP analysis indicated that depression, hemoglobin A1c (HbA1C), waist circumference, and anxiety were risk factors of self-compassion, while fasting blood-glucose (FBG) was a protective factor.
Conclusion: This study provides a reliable tool for identifying older adults with type 2 diabetes who may benefit from support. The findings suggest that healthcare providers should prioritize managing depression and anxiety, along with controlling HbA1c and waist circumference, to enhance self-compassion. These results can be translated into a practical risk scorecard to guide personalized care strategies in community health settings.
Keywords: self-compassion, community-dwelling older adults, type 2 diabetes, interpretable machine learning
1College of Medicine, Jiaxing University, Jiaxing, People’s Republic of China; 2Community Healthcare Center of Chengnan Sub-District, Jiaxing, People’s Republic of China; 3Community Healthcare Center of Jiabei Sub-District, Jiaxing, People’s Republic of China; 4Affiliated Hospital of Jiaxing University, Jiaxing, People’s Republic of China
Correspondence: Junxian Xu, College of Medicine, Jiaxing University, Jiaxing, Zhejiang, People’s Republic of China, Tel +86-13511357431, Email xujunxian_2022@163.com
Objective: Managing diabetes daily can be an emotional burden for older adults. Research shows that self-compassion, which refers to the ability to be kind and understanding toward oneself, can help improve emotional well-being. This study aimed to develop a machine learning prediction model to identify the influencing factors of self-compassion among community-dwelling older adults with type 2 diabetes.
Methods: We conducted this study in Jiaxing, China, during July and August 2024. We invited community-dwelling older adults with type 2 diabetes to complete a questionnaire that measured their levels of self-compassion, depression, and anxiety. Our goal was to find which of 26 different personal and health-related factors most influenced self-compassion. To achieve this, we used several machine learning algorithms to build and compare predictive models, selecting the best-performing one. Finally, we applied a technique called SHapley Additive exPlanations (SHAP) to clearly understand and interpret how each factor impacts self-compassion.
Results: The random forest model performed the best. SHAP analysis indicated that depression, hemoglobin A1c (HbA1C), waist circumference, and anxiety were risk factors of self-compassion, while fasting blood-glucose (FBG) was a protective factor.
Conclusion: This study provides a reliable tool for identifying older adults with type 2 diabetes who may benefit from support. The findings suggest that healthcare providers should prioritize managing depression and anxiety, along with controlling HbA1c and waist circumference, to enhance self-compassion. These results can be translated into a practical risk scorecard to guide personalized care strategies in community health settings.
Keywords: self-compassion, community-dwelling older adults, type 2 diabetes, interpretable machine learning