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FoodEstNet: Estimating True Food Consumption with Machine Learning

Darlington A. Akogo, Joseph B Danquah
doi: https://doi.org/10.1101/250506
Darlington A. Akogo
1minoHealth Al Labs, Accra, Ghana
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Joseph B Danquah
2(LD,RD)
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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.

  • Abbreviations

    Al
    Artificial Intelligence
    NCD
    non-communicable diseases.
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted January 19, 2018.
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    FoodEstNet: Estimating True Food Consumption with Machine Learning
    Darlington A. Akogo, Joseph B Danquah
    bioRxiv 250506; doi: https://doi.org/10.1101/250506
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    FoodEstNet: Estimating True Food Consumption with Machine Learning
    Darlington A. Akogo, Joseph B Danquah
    bioRxiv 250506; doi: https://doi.org/10.1101/250506

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