Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri

Butros M. Dahu (), Carlos I. Martinez-Villar, Imad Eddine Toubal, Mariam Alshehri, Anes Ouadou, Solaiman Khan, Lincoln R. Sheets and Grant J. Scott
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Butros M. Dahu: Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
Carlos I. Martinez-Villar: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Imad Eddine Toubal: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Mariam Alshehri: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Anes Ouadou: Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
Solaiman Khan: Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
Lincoln R. Sheets: Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
Grant J. Scott: Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA

IJERPH, 2024, vol. 21, issue 11, 1-23

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.

Keywords: DCNN; geospatial; machine learning; obesity rate; satellite imagery (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2024
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