Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data
BackgroundThe advancement of artificial intelligence technologies has opened new avenues for depression prevention and management in older adults with disability (defined by basic or instrumental activities of daily living, BADL/IADL). This study systematically developed machine learning (ML) models...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1624171/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849468406007332864 |
|---|---|
| author | Tongtong Jin Ayitijiang· Halili |
| author_facet | Tongtong Jin Ayitijiang· Halili |
| author_sort | Tongtong Jin |
| collection | DOAJ |
| description | BackgroundThe advancement of artificial intelligence technologies has opened new avenues for depression prevention and management in older adults with disability (defined by basic or instrumental activities of daily living, BADL/IADL). This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. A three-stage serial consensus approach feature selection framework (LASSO, Elastic Net, and Boruta) was employed to identify 21 robust predictors from 74 candidate variables. Ten ML algorithms were evaluated: LR, HistGBM, MLP, XGBoost, bagging, DT, LightGBM, RF, SVM, and CatBoost. Temporal external validation was performed using an independent 2018–2020 cohort to assess model generalizability. Performance was comprehensively evaluated using accuracy, AUC, F1-score, precision, and recall metrics. The SHAP framework was employed to interpret feature contribution mechanisms.ResultsResults demonstrated that the HistGBM model achieved optimal overall performance on the testing sets (AUC = 0.779, F1-score = 0.735, accuracy = 0.713), with only an 8.5% AUC difference between training and testing sets and a 10% difference between external validation and testing sets, indicating temporal stability. SHAP interpretability analysis revealed that sleep time (mean SHAP value = 0.344) in the health behavior domain and life satisfaction (0.339) and episodic memory (0.220) in the subjective perception domain contributed more significantly to prediction than traditional biomedical indicators.ConclusionThis study developed an AI-based tool for depression risk assessment in older adults with disability through a multi-stage feature selection process and a temporal external validation framework. These findings provide a practical screening instrument and a methodological reference for implementing AI technologies in geriatric mental health applications, thereby facilitating clinical translation of predictive analytics in this field. |
| format | Article |
| id | doaj-art-df9386ccc6fb457da396fef4565cc03f |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-df9386ccc6fb457da396fef4565cc03f2025-08-20T03:25:53ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-07-01810.3389/frai.2025.16241711624171Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS dataTongtong Jin0Ayitijiang· Halili1School of Law, Shanxi University of Finance and Economics, Taiyuan, ChinaCollege of Public Management (Law), Xinjiang Agricultural University, Urumqi, ChinaBackgroundThe advancement of artificial intelligence technologies has opened new avenues for depression prevention and management in older adults with disability (defined by basic or instrumental activities of daily living, BADL/IADL). This study systematically developed machine learning (ML) models to predict depression risk in disabled elderly individuals using longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), providing a potentially generalizable tool for early screening.MethodsThis study utilized longitudinal data from the CHARLS 2011–2015 cohort. A three-stage serial consensus approach feature selection framework (LASSO, Elastic Net, and Boruta) was employed to identify 21 robust predictors from 74 candidate variables. Ten ML algorithms were evaluated: LR, HistGBM, MLP, XGBoost, bagging, DT, LightGBM, RF, SVM, and CatBoost. Temporal external validation was performed using an independent 2018–2020 cohort to assess model generalizability. Performance was comprehensively evaluated using accuracy, AUC, F1-score, precision, and recall metrics. The SHAP framework was employed to interpret feature contribution mechanisms.ResultsResults demonstrated that the HistGBM model achieved optimal overall performance on the testing sets (AUC = 0.779, F1-score = 0.735, accuracy = 0.713), with only an 8.5% AUC difference between training and testing sets and a 10% difference between external validation and testing sets, indicating temporal stability. SHAP interpretability analysis revealed that sleep time (mean SHAP value = 0.344) in the health behavior domain and life satisfaction (0.339) and episodic memory (0.220) in the subjective perception domain contributed more significantly to prediction than traditional biomedical indicators.ConclusionThis study developed an AI-based tool for depression risk assessment in older adults with disability through a multi-stage feature selection process and a temporal external validation framework. These findings provide a practical screening instrument and a methodological reference for implementing AI technologies in geriatric mental health applications, thereby facilitating clinical translation of predictive analytics in this field.https://www.frontiersin.org/articles/10.3389/frai.2025.1624171/fulldisabled older adultsdepressionrisk predictionmachine learningCHARLSmental health LR |
| spellingShingle | Tongtong Jin Ayitijiang· Halili Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data Frontiers in Artificial Intelligence disabled older adults depression risk prediction machine learning CHARLS mental health LR |
| title | Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data |
| title_full | Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data |
| title_fullStr | Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data |
| title_full_unstemmed | Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data |
| title_short | Predicting the risk of depression in older adults with disability using machine learning: an analysis based on CHARLS data |
| title_sort | predicting the risk of depression in older adults with disability using machine learning an analysis based on charls data |
| topic | disabled older adults depression risk prediction machine learning CHARLS mental health LR |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1624171/full |
| work_keys_str_mv | AT tongtongjin predictingtheriskofdepressioninolderadultswithdisabilityusingmachinelearningananalysisbasedoncharlsdata AT ayitijianghalili predictingtheriskofdepressioninolderadultswithdisabilityusingmachinelearningananalysisbasedoncharlsdata |