Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri
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...
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| Veröffentlicht in: | International journal of environmental research and public health 2024-11, Vol.21 (11), p.1534 |
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| creator | Dahu, Butros M Martinez-Villar, Carlos I Toubal, Imad Eddine Alshehri, Mariam Ouadou, Anes Khan, Solaiman Sheets, Lincoln R Scott, Grant J |
| description | 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. |
| doi_str_mv | 10.3390/ijerph21111534 |
| format | Article |
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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.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph21111534</identifier><identifier>PMID: 39595801</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Adult ; Adults ; Algorithms ; Artificial intelligence ; Built environment ; Censuses ; Datasets ; Deep learning ; Exercise ; Female ; Generalized linear models ; Humans ; Machine Learning ; Male ; Missouri - epidemiology ; Neural networks ; Neural Networks, Computer ; Obesity ; Obesity - epidemiology ; Prevalence ; Public health ; Satellite Imagery</subject><ispartof>International journal of environmental research and public health, 2024-11, Vol.21 (11), p.1534</ispartof><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Adult Adults Algorithms Artificial intelligence Built environment Censuses Datasets Deep learning Exercise Female Generalized linear models Humans Machine Learning Male Missouri - epidemiology Neural networks Neural Networks, Computer Obesity Obesity - epidemiology Prevalence Public health Satellite Imagery |
| title | Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri |
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