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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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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