Explainable Machine Learning-Based Analysis for Identifying Risk Factors of Chronic Obstructive Pulmonary Disease

Chronic Obstructive Pulmonary Disease (COPD) is a major global health issue. Early detection and identification of risk factors are critical for effective management. This study applies explainable machine learning techniques using LIME and SHAP to analyze a large US dataset and provide interpretable insights into COPD risk factors. While established factors such as smoking and age remain central, the analysis also highlights the underappreciated influence of mental health, physical inactivity, and comorbid conditions. These insights enable a more holistic understanding of COPD risk profiles and support the development of tailored prevention strategies. While limited to US data, the findings pave the way for future studies incorporating diverse populations