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

Author

Listed:
  • Butros M. Dahu

    (Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
    Department of Health Management 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
    Department of Health Management 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
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA)

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.

Suggested Citation

  • Butros M. Dahu & Carlos I. Martinez-Villar & Imad Eddine Toubal & Mariam Alshehri & Anes Ouadou & Solaiman Khan & Lincoln R. Sheets & Grant J. Scott, 2024. "Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri," IJERPH, MDPI, vol. 21(11), pages 1-23, November.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:11:p:1534-:d:1524435
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    References listed on IDEAS

    as
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    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
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    5. Lynn Phan & Weijun Yu & Jessica M. Keralis & Krishay Mukhija & Pallavi Dwivedi & Kimberly D. Brunisholz & Mehran Javanmardi & Tolga Tasdizen & Quynh C. Nguyen, 2020. "Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States," IJERPH, MDPI, vol. 17(10), pages 1-10, May.
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    7. Butros M. Dahu & Khuder Alaboud & Avis Anya Nowbuth & Hunter M. Puckett & Grant J. Scott & Lincoln R. Sheets, 2023. "The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review," IJERPH, MDPI, vol. 20(5), pages 1-15, February.
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