Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology.

Background: Emphysema is a major contributor to lung disease progression and is associated with significant health risks, including exacerbations, mortality, and lung cancer. While environmental exposures, such as brominated flame retardants (BFRs), have been suggested as risk factors, their role in emphysema prediction has been largely overlooked. This study aimed to develop a machine learning (ML) model to predict emphysema risk incorporating BFRs exposure data and demographic characteristics. Methods: Using data from the NHANES (2005-2016) dataset, 8,205 participants were included in the study. The participants were divided into a training set (70%) and a testing set (30%). Eight machine learning algorithms, including lightGBM, MLP, DT, KNN, RF, SVM, Enet, and XGBoost, were applied to build and evaluate the model. Demographic data and BFRs exposure levels were used as predictors. SHAP and Partial Dependence Plots (PDP) were used for model interpretability analysis. Results: The MLP model showed the best performance with an AUC of 0.83. Age and PBB153 were identified as the most influential predictors. SHAP analysis revealed that higher exposure to BFRs, particularly PBB153, was strongly associated with increased emphysema risk. The WQS model further confirmed the positive relationship between BFRs exposure and emphysema. Conclusion: This study demonstrates the significant predictive value of BFR exposure in emphysema risk assessment and highlights the importance of incorporating environmental factors into disease prediction models. The findings provide new insights for integrating BFRs into personalized health risk assessments and public health interventions.