Asthma is a chronic inflammatory disorder that adversely affects the quality of life, particularly in older adults. The coexistence of depression in asthma patients complicates their management and exacerbates health outcomes. This study aims to develop a machine learning-based Depression Risk Identification Tool (DRIT) to predict depression risk in this population. We conducted a secondary analysis of data from the China Health and Retirement Longitudinal Study (CHARLS), including 1154 asthma patients. Using LASSO regression, we identified 21 significant predictors of depression. We evaluated eight machine learning algorithms, including the glmBoost model, which was selected based on performance metrics such as accuracy and area under the ROC curve (AUC). The glmBoost model demonstrated superior predictive performance, achieving an AUC of 0.740 (95% CI: 0.674–0.804) in the testing cohort and 0.664 (95% CI: 0.614–0.714) in the validation cohort. Key risk factors identified included poor cognitive function, heavy exercise, unmarried status, and female gender. The model's interpretability was enhanced using SHAP values, providing insights into the contributions of each predictor. The study's reliance on survey data may limit the comprehensiveness of risk factor identification. Additionally, the applicability of findings may vary across different populations, necessitating further validation in diverse cohorts. The DRIT effectively predicts depression risk among older asthma patients, enabling timely identification and intervention. This tool has the potential to improve patient outcomes and reduce the burden on healthcare systems by facilitating integrated management of asthma and depression.
An et al. (2025) studied this question.