Developing an Interpretable Machine Learning Predictive Model of Chronic obstructive pulmonary disease by Serum PFAS Concentration

Youmei Ying¹Ling Zhang²Yuting Wang^1*Xueqin Chen^3*

• ¹Nanjing Jiangbei Hospital, Affiliated Nanjing Jiangbei Hospital of Xinglin College, Nantong University, Nanjing, China
• ²Huai’an No. 3 People's Hospital, Huaian Second Clinical College of Xuzhou Medical University, Huaian, China
• ³The Affiliated Taizhou People's Hospital, Nanjing Medical University, Taizhou, China

Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, with limited early detection strategies. While previous studies have examined the relationship between per-and polyfluoroalkyl substances (PFAS) and COPD, limited research has applied interpretable machine learning (ML) techniques to this association.We investigated the association between PFAS exposure and COPD risk in 4450 NHANES participants from 2013 to 2018. After excluding missing covariates and extreme PFAS values and applying k-nearest neighbors (KNN) imputation, nine ML models, including CatBoost, were built and evaluated using metrics like accuracy, area under the curve (AUC), sensitivity, and specificity. The best-performing model was further analyzed using Partial Dependence Plots (PDP) and SHapley additive exPlanations (SHAP) analysis. To enhance clinical applicability, the final model was deployed as a publicly accessible web-based risk calculator.CatBoost emerged as the best model, achieving an accuracy of 84%, AUC of 0.89, sensitivity of 81%, and specificity of 84%. PDP revealed that higher PFOS and PFUA levels were associated with reduced COPD risk, whereas PFOA and MPAH showed positive associations with COPD. PFNA, PFDE, and PFHxS demonstrated mixed or non-linear effects. SHAP analysis provided insights into individual predictions and overall variable contributions, clarifying the complex PFAS-COPD relationship. The deployed web-based calculator enables interactive prediction and risk interpretation, supporting potential public health applications.CatBoost identified PFOS and PFUA as protective factors against COPD, while PFOA and MPAH increased risk of COPD. These findings emphasize the need for stricter PFAS regulation and highlight the potential of machine learning in guiding prevention strategies.