Developing an interpretable machine learning model for screening depression in older adults with functional disability.
Journal:
Journal of affective disorders
PMID:
40049534
Abstract
This study utilized data from the 2020 wave of the China Health and Retirement Longitudinal Study database, selecting 4322 participants aged 60 and above as the study sample. Important predictors of depression in older adults with functional disabilities were identified using LASSO regression, univariate logistic regression, and multivariate logistic regression. Five different machine learning algorithms-Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Naive Bayes-were employed to construct risk prediction models for depression in older adults with functional disabilities. The results indicated that Sleep duration, Age, Cognitive score, Gender, Residential area, Self-rated health, Arthritis, Gastrointestinal disease, Retirement status, Life satisfaction, Composite pain status, and Physical activity level were significant predictors of depression in this population. Both the Gradient Boosting model (accuracy: 0.69, precision: 0.70, recall: 0.79, F1-score: 0.74, AUC: 0.76) and the Logistic Regression model (accuracy: 0.68, precision: 0.68, recall: 0.79, F1-score: 0.73, AUC: 0.75) demonstrated good performance and strong generalizability. Additionally, SHAP interpretation was applied to the Gradient Boosting model to enhance the explainability of its predictions, while a nomogram was created for the Logistic Regression model to visually represent the predictive process, allowing for more intuitive understanding of the model's output. This study successfully developed a risk prediction model for depression in older adults with functional disabilities through machine learning. It provides a reference tool for community screening and clinical decision-making, helping to identify and manage depression risk within the older adults with functional disabilities.