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

Journal: PloS one
Published Date:

Abstract

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.

Authors

  • Ben Allen
    Department of Psychology, University of Kansas, 1415 Jayhawk Blvd, Lawrence, KS 66045, USA.