Applicability of machine learning techniques in food intake assessment: A systematic review

Abstract

The evaluation of food intake is important in scientific research and clinical practice to understand the relationship between diet and health conditions of an individual or a population. Large volumes of data are generated daily in the health sector. In this sense, Artificial Intelligence (AI) tools have been increasingly used, for example, the application of Machine Learning (ML) algorithms to extract useful information, find patterns, and predict diseases. This systematic review aimed to identify studies that used ML algorithms to assess food intake in different populations. A literature search was conducted using five electronic databases, and 36 studies met all criteria and were included. According to the results, there has been a growing interest in the use of ML algorithms in the area of nutrition in recent years. Also, supervised learning algorithms were the most used, and the most widely used method of nutritional assessment was the food frequency questionnaire. We observed a trend in using the data analysis programs, such as R and WEKA. The use of ML in nutrition is recent and challenging. Therefore, it is encouraged that more studies are carried out relating these themes for the development of food reeducation programs and public policies.

Keywords:

• Food intake
• diet
• artificial intelligence
• machine learning
• supervised and unsupervised algorithms
• computational tools

Author contributions

L.O.C., A.L.G.D., D.L.F., and J.B. designed the study. L.O.C and A.L.G.D. selected and reviewed the articles and extracted the data. L.O.C., A.L.G.D., and D.L.F. analyzed and interpreted the data and drafted the manuscript. J.B., R.D-B., and F.R.C improved the manuscript and critically revised the scientific content. All authors read and approved the final manuscript.

Conflict of interest

The authors have no relevant interests to declare.

Additional information

Funding

This work was supported by the Fundação de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG), Belo Horizonte, Brazil; the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brasilia, Brazil; and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brasilia, Brazil.