Identifying the Most Crucial Factors Influencing Self-Compassion Among Community-Dwelling Older Adults with Type 2 Diabetes Using Interpretable Machine Learning.

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.