bioRxivPhysiologyPreprint
FoodEstNet: Estimating True Food Consumption with Machine Learning
Abstract
We developed a Machine Learning/Artificial Intelligence model that estimates how much of a food type a person truly consumes. People tend to underestimate how much they consume which makes the work of nutritionists and dietitians difficult since they rely on food estimates for food portion size control and nutritional management of diseases. We trained an XGBoost model to estimate how much a patient truly consumes based on Age, Sex, BMI, socioeconomic status and perceived consumption.
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