De Novo exposomic geospatial assembly of chronic disease regions with machine learning & network analysis.

ABSTRACT:

Background

Determining spatial relationships between diseases and the exposome is limited by available methodologies. aPEER (algorithm for Projection of Exposome and Epidemiological Relationships) uses machine learning (ML) and network analysis to find spatial relationships between diseases and the exposome in the United States.

Methods

Using aPEER we examined the relationship between 12 chronic diseases and 186 pollutants. PCA, K-means clustering, and map projection produced clusters of counties derived from pollutants, and the Jaccard correlation between these clusters with chronic disease geography (defined as groups of counties with high chronic disease prevalence rates) was calculated. Disease-pollution correlation matrices were used together with network analysis to id

SUBMITTER: Deonarine A 

PROVIDER: S-EPMC11833148 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature