Machine learning prediction of glaucoma by heavy metal exposure: results from the National Health and Nutrition Examination Survey 2005 to 2008.

ABSTRACT: Using follow-up data from the National Health and Nutrition Examination Survey (NHANES) database, we have collected information on 2572 subjects and used generalized linear model to investigate the association between urinary heavy metal levels and glaucoma risk. In addition, we have developed an individualized risk prediction model using machine learning algorithms and further interpreted the model results through feature importance analysis, local cumulative analysis, and interaction effects. In this study, we found significant association between logarithmically calculated arsenic (As) metabolites, especially arsenochlorine (AC), and glaucoma after adjusting for a series of confounders, including urinary creatinine (β = 1.090, 95% CI: 0.313-1.835). The Shapley Additive Explanations (SHA

SUBMITTER: Wang X 

PROVIDER: S-EPMC11811145 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature