Machine Learning in Nutrition Research.

Data currently generated in the field of nutrition is becoming increasingly complex and high-dimensional, bringing with it new methods of data analysis. The characteristics of machine learning make it suitable for such analysis and thus lends itself as an alternative tool to deal with data of this nature. Machine learning 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 machine learning, which limits its application and therefore potential to solve currently open questions. Thus, 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. Machine learning 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 machine learning is particularly applicable, precision nutrition and metabolomics, are then presented. Finally, a framework is first outlined to guide the interested researcher in integrating machine learning into their work. By acting as a resource to which researchers can refer, we hope to support the integration of machine learning in the field of nutrition to facilitate modern research.