Abstract
Using food recall data collected from over 8000 Americans, we used Machine Learning to classify a person’s demographic characteristics (age, gender, race, and income) based on the foods they consumed in a 24-h period. The best-performing models predicted gender correctly 61%, race 44%, and age 43% of the time on independent validation data. The model was subsequently used to provide tailored recommendations for healthier food options based on low-calorie and low-fat options for specific food groups that are typically consumed by other Americans that match the person’s demographics. This system is part of a larger Smart Human-Centered System that assists users in recording the foods they consume, recognizes nutritional content of the food, offers tailored recommendations for consuming healthier foods, and tracking behavior change over time.
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References
Centers for Disease Control and Prevention. https://www.cdc.gov/obesity/data/adult.html
Seto, E., Hua, J., Wu, L., Shia, V., Eom, S., Wang, M., Li, Y.: Models of individual dietary behavior based on smartphone data: the influence of routine, physical activity, emotion, and food environment. PLoS ONE 11(4), e0153085 (2016)
Boushey, C.J., Kerr, D.A., Wright, J., Lutes, K.D., Ebert, D.S., Delp, E.J.: Use of technology in children’s dietary assessment. Eur. J. Clin. Nutr. 63(S1), S50 (2009)
Kim, J., Lin, S., Ferrara, G., Hua, J., Seto, E.: Validation of a food image classifier based on the top 50 consumed foods from the what we eat in America survey. In: IEEE Biomedical Health Informatics 2019 Conference, Chicago, 19–22 May (2019)
Heo, S., Moser, D.K., Lennie, T.A., Payne-Emerson, H., Welch, J.L., Weaver, M.: Development and testing of the feasibility and acceptability of a tailored dietary intervention in patients with heart failure. J. Cardiovasc. Nurs. 30(3), 213–221 (2015)
Gans, K.M., Risica, P.M., Strolla, L.O., Fournier, L., Kirtania, U., Upegui, D., Zhao, J., George, T., Acharyya, S.: Effectiveness of different methods for delivering tailored nutrition education to low income, ethnically diverse adults. Int. J. Behav. Nutr. Phys. Act. 6(1), 24 (2009)
Dwyer, J., Picciano, M.F., Raiten, D.J.: Collection of Food and Dietary Supplement Intake Data: What We Eat in America–NHANES. J. Nutr. 133, 590S–600S (2003)
Archer, E., Pavela, G., Lavie, C.J.: The inadmissibility of what we eat in America and NHANES dietary data in nutrition and obesity research and the scientific formulation of national dietary guidelines. Mayo Clin. Proc. 90(7), 911–926 (2015)
Acknowledgments
Research supported by NIH grant U54 MD011240 and NIH grant 5P30 ES007033-23. JH is supported by NIH NHLBI T32 HL007034.
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Kim, J., Lin, S., Ferrara, G., Hua, J., Seto, E. (2020). Identifying People Based on Machine Learning Classification of Foods Consumed in Order to Offer Tailored Healthier Food Options. In: Ahram, T., Karwowski, W., Vergnano, A., Leali, F., Taiar, R. (eds) Intelligent Human Systems Integration 2020. IHSI 2020. Advances in Intelligent Systems and Computing, vol 1131. Springer, Cham. https://doi.org/10.1007/978-3-030-39512-4_30
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DOI: https://doi.org/10.1007/978-3-030-39512-4_30
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