Hernández-Hernández David Jovani, Perez-Lizaur Ana Bertha, Palacios-González Berenice, Morales-Luna Gesuri
Departamento de Física y Matemáticas, Universidad Iberoamericana Ciudad de México, Ciudad de México, Mexico.
Departamento de Salud, Universidad Iberoamericana Ciudad de México, Ciudad de México, Mexico.
Front Nutr. 2023 Aug 10;10:1231873. doi: 10.3389/fnut.2023.1231873. eCollection 2023.
Food Exchange Lists (FELs) are a user-friendly tool developed to help individuals aid healthy eating habits and follow a specific diet plan. Given the rapidly increasing number of new products or access to new foods, one of the biggest challenges for FELs is being outdated. Supervised machine learning 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.
Data mining techniques were used to generate the algorithm, which consists of processing and analyzing the information to find patterns, trends, or repetitive rules that explain the behavior of the data in a food database after performing this task. It was decided to approach the problem from a vector formulation (through 9 nutrient dimensions) 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% at the first three places. Also, the algorithm indicates which foods exceed the limits established in sodium, sugar, and/or fat content and show their equivalents.
Accurate and robust FELs could improve implementation and adherence to the recommended diet. Compared with manual categorization and calculation, machine learning approaches have several advantages. Machine learning reduces the time needed for manual food categorization and equivalent portion calculation of many food products. 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, machine learning 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. As a neural network, this model could be trained with other food bases and thus improve its predictive capacity. 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.
食物交换份列表（FELs）是一种方便用户的工具，旨在帮助个人养成健康的饮食习惯并遵循特定的饮食计划。鉴于新产品数量迅速增加或可获取新食物，FELs面临的最大挑战之一是过时。监督机器学习算法可能是促进这一过程并实现FELs更新的一种工具——本研究旨在生成一种算法来预测食物分类并计算等量份数。
使用数据挖掘技术生成算法，该算法包括处理和分析信息以找到模式、趋势或重复规则，这些规则可解释在执行此任务后食物数据库中数据的行为。决定从向量公式（通过9个营养维度）入手解决该问题，这导致了诸如球形K均值（SKM）等分类器的提议，并通过发展这一想法，借助多层感知器（MLP）使分类器的边界更加平滑，将其与另外两种机器学习算法（随机森林和XGBoost）进行比较。
本研究提出的算法可以对单一食物或食物列表进行分类并计算其等量份数。该算法能够以97%的置信度对一千多种食物进行前三类别的分类。此外，该算法还能指出哪些食物的钠、糖和/或脂肪含量超出既定限量，并显示其等量食物。
准确且可靠的FELs可以改善对推荐饮食的实施和依从性。与人工分类和计算相比，机器学习方法具有多个优势。机器学习减少了对许多食品进行人工食物分类和计算等量份数所需的时间。由于可以访问不同人群的食物成分数据库，我们的算法可以进行调整并应用于其他数据库，提供更多样化的区域产品和食物。总之，机器学习是一种在生成FELs方面很有前景的自动化方法。本研究提供了一种大规模、准确的实时处理算法的证据，该算法可用于设计针对人群所消费食物的膳食计划。我们的模型不仅能够区分和分类组内或子组内的食物，还能进行等量食物的计算。作为一个神经网络，该模型可以用其他食物库进行训练，从而提高其预测能力。尽管SKM模型的性能与其他类型的分类器相比更低，但我们的模型允许从一个并非先前由机器学习分类的组中选择等量食物，而是使用一种完全可解释的算法，如余弦相似度来比较食物。
