Machine Learning in Nutrition Research

Abstract

Data currently generated in the field of nutrition are becoming increasingly complex and high-dimensional, bringing with them new methods of data analysis. The characteristics of machine learning (ML) make it suitable for such analysis and thus lend itself as an alternative tool to deal with data of this nature. ML has already been applied in important problem areas in nutrition, such as obesity, metabolic health, and malnutrition. Despite this, experts in nutrition are often without an understanding of ML, which limits its application and therefore potential to solve currently open questions. The current article aims to bridge this knowledge gap by supplying nutrition researchers with a resource to facilitate the use of ML in their research. ML is first explained and distinguished from existing solutions, with key examples of applications in the nutrition literature provided. Two case studies of domains in which ML is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is outlined to guide interested researchers in integrating ML into their work. By acting as a resource to which researchers can refer, we hope to support the integration of ML in the field of nutrition to facilitate modern research.

Keywords: XGBoost; cardiovascular disease; diabetes; machine learning; models; obesity; omics; personalized nutrition; random forest.

FIGURE 1

Four types of machine learning. In supervised learning, labels are provided in the data for objective evaluation of algorithm performance, whereas in unsupervised learning, the algorithm partitions the data based on similarity. In semi-supervised, only a portion of the data comes with labels, although all data are eventually classified. Reinforcement learning makes use of penalties and rewards in a dynamic environment to train the algorithm.

FIGURE 2

Various validation techniques. Data split simply consists of excluding a portion of the data for testing after training. In k-fold cross-validation, the data are split into k number of folds, and each fold is used once for training and k – 1 times for training. Leave-one-out cross-validation uses the same concept except that k is equal to the number of data samples, so each individual sample is used once for testing and n – 1 times for training. Stratified cross-validation ensures that the proportions of classes remain the same in each split (training and test) and each fold. Finally, external validation consists of using data different from those on which the algorithm was trained.

FIGURE 3

The major components of the omics field and their proximity to the phenotype.