Studying children food exposure and food consumption using deep learning
Abstract
Children's eating behavior is one of the main pillars of a healthy life. Recent studies show that eating unhealthy food is highly associated with many chronic diseases including diabetes, obesity, and cancer. Such dietary habits are often shaped by complex factors influenced by the children's home, school, and neighborhood environments. However, studying the eating behaviors of children and analyzing the factors affecting them is currently done using traditional questionnaire-based methods, which often suffer from recall and bias issues. In this thesis, we developed a comprehensive approach to study children's food exposure and food consumption using deep learning.
Description
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Thesis
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Keywords
DEEP LEARNING, MACHINE LEARNING, CHILD NUTRITION, CHILD HEALTH, FOOD CONSUMPTION