AUTHOR=Hernández-Hernández David Jovani , Perez-Lizaur Ana Bertha , Palacios-González Berenice , Morales-Luna Gesuri 

TITLE=Machine learning accurately predicts food exchange list and the exchangeable portion

JOURNAL=Frontiers in Nutrition

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2023.1231873

DOI=10.3389/fnut.2023.1231873

ISSN=2296-861X

ABSTRACT=Introduction: Food Exchange Lists (FELs) are a tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Supervised machine learning (ML) algorithms could be a tool that facilitates this process and allows for updated FELs. The present study aimed to generate an algorithm to predict food classification and calculate the equivalent portion. Methods: Data mining techniques were used to generate the algorithm. It was decided to approach the problem from a vector formulation that led to proposals for classifiers such as Spherical K-Means (SKM), and by developing this idea, it was possible to smooth the limits of the classifier with the help of a Multilayer Perceptron (MLP) which were compared with two other algorithms of machine learning, these being Random Forest and XGBoost.The algorithm proposed in this study could classify and calculate the equivalent portion of a single or a list of foods. The algorithm allows the categorization of more than one thousand foods with a confidence level of 97%. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.Discussion: ML approaches have several advantages compared to manual categorization and calculation. Since it is possible to access food composition databases of various populations, our algorithm could be adapted and applied in other databases, offering an even greater diversity of regional products and foods. In conclusion, ML is a promising method for automation in generating FELs. This study provides evidence of a large-scale, accurate real-time processing algorithm that can be useful for designing meal plans tailored to the foods consumed by the population. Our model allowed us not only to distinguish and classify foods within a group or subgroup but also to perform the calculation of an equivalent food. Although the performance of the SKM model was lower compared to other types of classifiers, our model allows selecting an equivalent food not from a group previously classified by machine learning but with a fully interpretable algorithm such as cosine similarity for comparing food.