This paper is in the following e-collection/theme issue:

Applications of AI (130) Artificial Intelligence (1556) Public (e)Health, Digital Epidemiology and Public Health Informatics (811) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (119) Obesity and Nutrition as Public Health Problem (323) mHealth for Data Collection and Research (1018) Development and Evaluation of Research Methods, Instruments and Tools (572) Big Data (262) Machine Learning (59)

Published on in Vol 3 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64362, first published .
Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

Authors of this article:

Butros M Dahu1 Author Orcid Image ;   Solaiman Khan1 Author Orcid Image ;   Imad Eddine Toubal1 Author Orcid Image ;   Mariam Alshehri1 Author Orcid Image ;   Carlos I Martinez-Villar1 Author Orcid Image ;   Olabode B Ogundele1 Author Orcid Image ;   Lincoln R Sheets1 Author Orcid Image ;   Grant J Scott1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Butros M Dahu   1 , PhD ;   Solaiman Khan   1 , MA ;   Imad Eddine Toubal   1 , PhD ;   Mariam Alshehri   1 , PhD ;   Carlos I Martinez-Villar   1 , BSc ;   Olabode B Ogundele   1 , PhD ;   Lincoln R Sheets   1 , MD, PhD ;   Grant J Scott   1 , PhD

1 University of Missouri, Institute for Data Science and Informatics, Columbia, MO, United States

Corresponding Author:

  • Butros M Dahu, PhD
  • University of Missouri
  • Institute for Data Science and Informatics
  • Columbia, MO
  • United States
  • Phone: 1 8325124825
  • Email: peterdahu@gmail.com