Effects of environmental phenols on eGFR: machine learning modeling methods applied to cross-sectional studies.

ABSTRACT:

Purpose

Limited investigation is available on the correlation between environmental phenols' exposure and estimated glomerular filtration rate (eGFR). Our target is established a robust and explainable machine learning (ML) model that associates environmental phenols' exposure with eGFR.

Methods

Our datasets for constructing the associations between environmental phenols' and eGFR were collected from the National Health and Nutrition Examination Survey (NHANES, 2013-2016). Five ML models were contained and fine-tuned to eGFR regression by phenols' exposure. Regression evaluation metrics were used to extract the limitation of the models. The most effective model was then utilized for regression, with interpretation of its features carried out using shapley additive explanatio

SUBMITTER: Liu L 

PROVIDER: S-EPMC11324456 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature