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Machine learning prediction of the degree of food processing

Food Science and Technology

Machine learning prediction of the degree of food processing

G. Menichetti, B. Ravandi, et al.

Discover how FoodProX, a groundbreaking machine learning algorithm developed by Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian, and Albert-László Barabási, reveals that over 73% of the US food supply is ultra-processed. This research highlights alarming connections between ultra-processed food consumption and serious health risks, emphasizing the urgent need for accessible information on food processing to enhance population health.

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Playback language: English
Abstract
This paper introduces FoodProX, a machine learning algorithm that predicts the degree of food processing. It finds that over 73% of the US food supply is ultra-processed and demonstrates a correlation between ultra-processed food consumption and increased risk of metabolic syndrome, diabetes, angina, elevated blood pressure, biological age, and reduced vitamin bioavailability. Replacing ultra-processed foods with less processed alternatives shows significant health improvements, highlighting the potential of accessible processing information to enhance population health.
Publisher
Nature Communications
Published On
Apr 21, 2023
Authors
Giulia Menichetti, Babak Ravandi, Dariush Mozaffarian, Albert-László Barabási
Tags
food processing
ultra-processed foods
health risks
machine learning
population health
metabolic syndrome

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