An interpretable ensemble learning framework for COPD classification using population-based clinical and laboratory data from NHANES.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) represents the third leading cause of death globally, affecting 213.39 million people, yet approximately 70% of cases remain undiagnosed due to insufficient spirometry utilization in primary care settings. METHODS: We developed machine learning-based COPD classification models using nationally representative data from 8061 participants aged ≥40 years in the National Health and Nutrition Examination Survey 2007-2012. Least Absolute Shrinkage and Selection Operator (LASSO) regression with repeated ten-fold cross-validation systematically selected sixteen features from 33 candidate variables. We compared twelve machine learning algorithms. SHapley Additive exPlanations (SHAP) analysis provided model interpretability. RESULTS: CatBoost demonstrated optimal performance on the validation set with an area under the receiver operating characteristic curve (AUC) of 0.788, a sensitivity of 0.706, and a specificity of 0.728. SHAP analysis identified age and smoking duration as dominant risk factors, revealing non-linear relationships, including body mass index's U-shaped association with COPD risk and synergistic age-smoking interactions. Decision Curve Analysis confirmed net clinical benefit across risk thresholds of 0.1 to 0.6. CONCLUSIONS: This study establishes a machine learning framework combining systematic feature selection, comprehensive algorithm evaluation, and explainable artificial intelligence for COPD risk classification using readily available clinical data. The model achieved a clinically appropriate sensitivity-specificity balance through threshold optimization, demonstrating potential for risk-stratified spirometry referral in primary care settings. External validation in independent populations represents the essential next step before clinical implementation.