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

Applications of AI (272) Artificial Intelligence (2064) Public (e)Health, Digital Epidemiology and Public Health Informatics (978) GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (133) Obesity and Nutrition as Public Health Problem (392) mHealth for Data Collection and Research (1184) Development and Evaluation of Research Methods, Instruments and Tools (727) Big Data (306)

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 (1) Tweetations (1) Metrics

Journals

  1. Tanoto P, Ye H, Saffari S, Leow Y, Vipin A, Lee F, Ghildiyal S, Liew S, Mohammed A, Sandhu G, Aravindhan K, Bhalla G, Shaik Mohamed Salim R, Kandiah N. Detection of Vascular Mild Cognitive Impairment in Southeast Asia Using the Visual Cognitive Assessment Test: Machine Learning Analysis From the BIOCIS (Biomarkers and Cognition Study, Singapore). JMIR Aging 2025;8:e76847 View