An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence

BACKGROUND: There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box. OBJECTIVE: The goal of...

Descripción completa

Detalles Bibliográficos
Autor principal: Allen, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553328/
https://www.ncbi.nlm.nih.gov/pubmed/37796874
http://dx.doi.org/10.1371/journal.pone.0292341

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10553328/
https://www.ncbi.nlm.nih.gov/pubmed/37796874
http://dx.doi.org/10.1371/journal.pone.0292341

Ejemplares similares