Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells

Autor:	Seth Frndak, Fengxia Yan, Mike Edelson, Lilly Cheng Immergluck, Katarzyna Kordas, Muhammed Y. Idris, Carmen M. Dickinson-Copeland
Rok vydání:	2023
Předmět:	machine learning geographic prediction Health Toxicology and Mutagenesis lead exposure primary prevention Public Health Environmental and Occupational Health
Zdroj:	International Journal of Environmental Research and Public Health Volume 20 Issue 5 Pages: 4477
ISSN:	1660-4601
DOI:	10.3390/ijerph20054477
Popis:	Low-level lead exposure in children is a major public health issue. Higher-resolution spatial targeting would significantly improve county and state-wide policies and programs for lead exposure prevention that generally intervene across large geographic areas. We use stack-ensemble machine learning, including an elastic net generalized linear model, gradient-boosted machine, and deep neural network, to predict the number of children with venous blood lead levels (BLLs) ≥2 to
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d888a4f9ab84d5f61cf440e023d60dba https://doi.org/10.3390/ijerph20054477 Zobrazit plný text záznamu