Machine learning prediction model with shap interpretation for chronic bronchitis risk assessment based on heavy metal exposure: a nationally representative study.

Abstract

Background: Chronic bronchitis (CB), as a core precursor of Chronic Obstructive Pulmonary Disease (COPD), is crucial for global disease burden prevention and control. Although the association between heavy metal exposure and respiratory damage has been preliminarily demonstrated, traditional linear models are difficult to resolve the nonlinear interactions and dose-response heterogeneity. The aim of this study was to construct the first heavy metal exposure-chronic bronchitis risk prediction model by integrating exposureomics data through machine learning (ML).

Methods: Weighted logistic regression was used to assess the association of 14 blood and urine heavy metals with CB based on nationally representative samples from the 2005-2015 National Health and Nutrition Examination Survey (NHANES). The Boruta algorithm was further applied to screen the characteristic variables and construct 10 ML models. The best model was selected by four evaluation metrics: accuracy, specificity, sensitivity, and area under the ROC curve (AUC), and the best model was visually interpreted using Shapley's additive interpretation (SHAP).

Results: The multifactorial logistic regression model showed that urinary cadmium (OR = 1.53, 95% CI = 1.17-1.98) versus blood cadmium (OR = 1.36, 1.13-1.65) was an independent risk factor for CB. The CatBoost model had the best predictive performance (AUC = 0.805), with smoking as the most significant predictor, followed by blood cadmium concentration and gender.

Conclusion: In this research, the first risk prediction diagnostic model for heavy metal-chronic bronchitis was developed, in which CatBoost model had the best performance, and it provides a referenceable prediction model for the screening of high-risk groups.