ArticleInternational journal of environmental research and public health2024
Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.
Article in International journal of environmental research and public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Harnessing Geospatial Artificial Intelligence (GeoAI) for Environmental Epidemiology: A Narrative Review.Current environmental health reports · 2025Review
- Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.International journal of environmental research and public health · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC's 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts.
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