Machine learning model for depression based on heavy metals among aging people: A study with National Health and Nutrition Examination Survey 2017-2018.

ABSTRACT:

Objective

To explore the association between depression and blood metal elements, we conducted this machine learning model fitting research.

Methods

Datasets from the National Health and Nutrition Examination Survey (NHANES) in 2017-2018 were downloaded (https://www.cdc.gov/nchs/nhanes). After screening, 3,247 aging samples with 10 different metals [lead (Pb), mercury (Hg), cadmium (Cd), manganese (Mn), selenium (Se), chromium (Cr), cobalt (Co), inorganic mercury (InHg), methylmercury (MeHg) and ethyl mercury (EtHg)] were included. Eight machine learning algorithms were compared for analyzing metal and depression. After comparison, XGBoost showed optimal effects. Poisson regression and XGBoost model (a kind of decision tree algorithm) were conducted to find the risk factors

SUBMITTER: Xia F 

PROVIDER: S-EPMC9386350 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature