Developing an interpretable machine learning predictive model of chronic obstructive pulmonary disease by serum PFAS concentration.

ABSTRACT:

Background

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.

Methods

We investigated the association between PFAS exposure and COPD risk in 4,450 National Health and Nutrition Examination Survey (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

SUBMITTER: Shao X 

PROVIDER: S-EPMC12287074 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature