Using Big Data and Machine Learning for Multilayered Surveillance for Healthy Food Environment and Diet
Conference
Belkhiria, F, Nie, JY, Paquet, C et al. (2022). Using Big Data and Machine Learning for Multilayered Surveillance for Healthy Food Environment and Diet
. 4055-4064. 10.1109/BigData55660.2022.10020762
Belkhiria, F, Nie, JY, Paquet, C et al. (2022). Using Big Data and Machine Learning for Multilayered Surveillance for Healthy Food Environment and Diet
. 4055-4064. 10.1109/BigData55660.2022.10020762
As a means to understanding the healthiness of the food environment, obtaining big data (big food and other types) to model the built environment becomes critical. In this paper, we train and test seven different ML methods on bigdata from census data to predict the healthiness of the food environment. We introduce a synthetic ecosystem platform that can be used to bridge big data of different types combined with ML method for supporting food environment surveillance and intervention simulations. We illustrate with an example of neighborhood-level healthfulness assessment and conclude by a presentation of our next steps on employing machine learning to classify diet quality and recommend healthier food options to consumers.
