Key result
A Support Vector Machine (SVM) model successfully predicted depression risk in COPD patients, achieving an AUC of 0.887 in the independent test set.
Why the study?
COPD is often accompanied by depression, which exacerbates disease burden and impairs quality of life, but early identification of depression risk in these patients remains a clinical challenge.
Does a machine learning-based model accurately predict depression risk in patients with COPD?
Cross-Sectional (n=1,638)
Does a machine learning-based model accurately predict depression risk in patients with COPD?
Effect estimate: AUC 0.887
An SVM-based machine learning model utilizing routinely collected clinical and demographic data can accurately predict depression risk in COPD patients, offering a tool for early screening.
May aid early depression screening in COPD; leaves open prospective validation before clinical adoption.
BACKGROUND: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory condition often accompanied by depression, which exacerbates disease burden and impairs quality of life. Early identification of depression risk in COPD patients remains a clinical challenge. OBJECTIVE: This study aimed to develop a machine learning-based model to predict depression risk in COPD patients, utilizing interpretable features from clinical and demographic data to support early intervention. METHODS: Data were extracted from the National Health and Nutrition Examination Survey (NHANES), involving 1,638 COPD patients. Depression was assessed using the Patient Health Questionnaire-9 (PHQ-9) scale. Feature selection was performed with Boruta and least absolute shrinkage and selection operator (LASSO) algorithms, identifying key predictors from demographic, lifestyle, medical history, and laboratory variables. Nine machine learning models were trained and evaluated, with performance assessed via accuracy, area under the curve (AUC), calibration, and clinical utility metrics. RESULTS: Significant predictors of depression included sleep disturbances, age, poverty, hypertension, and comorbidities like cardiovascular disease. The Support Vector Machine (SVM) model achieved the highest performance, with an AUC of 0.890 in the validation set and 0.887 in the test set, demonstrating robust discriminative ability and clinical applicability. SHapley Additive exPlanations (SHAP) analysis enhanced model interpretability. Notably, sleep disturbances, younger age, and greater socioeconomic deprivation were associated with an elevated risk of depression. CONCLUSION: This study presents a reliable SVM-based model for predicting depression risk in COPD patients, leveraging NHANES data and interpretable features. It offers a valuable tool for early screening and personalized care, with potential to improve mental health outcomes in this population.
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Feng et al. (2025) conducted a cross-sectional in Chronic Obstructive Pulmonary Disease (COPD) and Depression (n=1,638). Support Vector Machine (SVM) predictive model vs. Other machine learning models was evaluated on Area under the curve (AUC) for predicting depression risk in the test set (AUC 0.887). A Support Vector Machine (SVM) model successfully predicted depression risk in COPD patients, achieving an AUC of 0.887 in the independent test set.
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