2型糖尿病の地域在住高齢者における自己肯定感に影響を与える最も重要な要因を解釈可能な機械学習を用いて特定する
Junxian Xu1, Jianzhong Yang2, Yuping Lu3
1College of Medicine, Jiaxing University, Jiaxing, People's Republic of China.
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.
