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Comparison of logistic regression and machine learning methods for predicting depression risks among disabled elderly individuals: results from the China Health and Retirement Longitudinal Study
Comparing traditional and machine learning methods for predicting depression risk in disabled older adults in China
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Abstract
A predictive model achieved area under the curve (AUC) values of 0.76 in training and 0.73 in validation for identifying depression risks in 3,107 disabled elderly individuals.
- Poor self-rated health, pain, lack of caregivers, cognitive impairment, and shorter sleep duration are linked to increased depression risk in this population.
- The XGBoost model outperformed other models during training, while logistic regression showed better results during validation.
- A good model fit was indicated by the calibration curve and a Brier score of 0.20.
- Decision curve analysis supports the model's clinical utility in assessing depression risks.
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Key numbers
1,774 of 3,107
Prevalence of Depression
Total number of disabled elderly individuals with depression out of the sample size.
0.76
AUC for XGBoost Model
Area under the curve for the XGBoost model in the training set.
0.73
AUC for Logistic Regression Model
Area under the curve for the logistic regression model in the validation set.