Leading Predictors of Depression among Working Age Adults with Cognitive Limitations: An Interpretable Machine Learning Approach

Abstract

Objectives: There are research knowledge gaps in depression in adults with cognitive disabilities. This study identified the leading predictors and their associations with depression working age adults with cognitive disabilities using machine learning methods. Methods: This cross-sectional study used data from the 2022 National Health Interview Survey and included working age adults (18-64 years) with cognitive disabilities (weighted N=464,453). We employed eXtreme Gradient Boosting (XGBoost) regression to determine key predictors. Global and local interpretations of associations were performed using SHapley Additive exPlanations (SHAP). Our predictive model used 20 features such as age, health status, and social determinants of health (SDoH) such as education, and poverty. The model building steps included 70% training and 30% testing split of the data, 10-fold cross-validations, and six rounds of optimization using Python 3.9.12. Model performance was evaluated using the test dataset. Results: Model performance metrics was: area under the curve (0.83,0.77) for training and test curves, respectively. The top 10 leading predictors of depression in working age adults with cognitive disabilities included less than high school, high income, employment, health insurance, marital status, and health status. SHAP plots suggested a complex relationship between age and depression as well as race and depression. Marital status and perceived physical health were associated with lower depression. Being a smoker was associated with higher depression. Conclusions: 1 in 2 adults with cognitive disabilities reported depression in 2022. SDoH were some of the leading predictors of depression. Our findings suggest developing policies that target SDoH to reduce the risk of depression and promote optimal treatment for mental health. The model performance was good with the AUC as 0.83