Hyperparameter settings of each models.
<div><p>Objective</p><p>To develop a predictive model for evaluating depression among middle-aged and elderly individuals in China.</p><p>Methods</p><p>Participants aged ≥ 45 from the 2020 China Health and Retirement Survey (CHARLS) cross-sectional stu...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| الملخص: | <div><p>Objective</p><p>To develop a predictive model for evaluating depression among middle-aged and elderly individuals in China.</p><p>Methods</p><p>Participants aged ≥ 45 from the 2020 China Health and Retirement Survey (CHARLS) cross-sectional study were enrolled. Depressive mood was defined as a score of 10 or higher on the CESD-10 scale, which has a maximum score of 30. A predictive model was developed using five selected machine learning algorithms. The model was trained and validated on the 2020 database cohort and externally validated through a questionnaire survey of middle-aged and elderly individuals in Shaanxi Province, China, following the same criteria. SHapley Additive Interpretation (SHAP) was employed to assess the importance of predictive factors.</p><p>Results</p><p>The stacked ensemble model demonstrated an AUC of 0.8021 in the test set of the training cohort for predicting depressive symptoms; the corresponding AUC in the external validation cohort was 0.7448, outperforming all base models.</p><p>Conclusion</p><p>The stacked ensemble approach serves as an effective tool for identifying depression in a large population of middle-aged and elderly individuals in China. For depression prediction, factors such as life satisfaction, self-reported health, pain, sleep duration, and cognitive function are identified as highly significant predictive factors.</p></div> |
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