Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study
This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model's high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.
Authors
- Imad Eddine Toubal (ORCID: https://orcid.org/0000-0003-1754-0823)
- Butros M. Dahu (ORCID: https://orcid.org/0000-0001-5880-2450)
- Lincoln Sheets (ORCID: https://orcid.org/0000-0002-1586-1474)
- Grant J. Scott (ORCID: https://orcid.org/0000-0001-5870-9387)
- Solaiman Khan (ORCID: https://orcid.org/0000-0002-8059-9783)
- Mariam Alshehri (ORCID: https://orcid.org/0000-0002-4035-7771)
- Olabode B. Ogundele (ORCID: https://orcid.org/0000-0003-2249-1181)
- Carlos I. Martinez-Villar (ORCID: https://orcid.org/0000-0002-6842-3742)
Institutions
- University of Missouri (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2024-11-15
- DOI
- https://doi.org/10.2196/64362
- Citations
- 1
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.54
Funders
- National Science Foundation