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Applicability of machine learning techniques in food intake assessment: A systematic review.
Oliveira Chaves, Larissa; Gomes Domingos, Ana Luiza; Louzada Fernandes, Daniel; Ribeiro Cerqueira, Fabio; Siqueira-Batista, Rodrigo; Bressan, Josefina.
Afiliação
  • Oliveira Chaves L; Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Brazil.
  • Gomes Domingos AL; Department of Nutrition and Health, Universidade Federal de Viçosa, Viçosa, Brazil.
  • Louzada Fernandes D; Department of Informatics, Universidade Federal de Viçosa, Viçosa, Brazil.
  • Ribeiro Cerqueira F; Department of Production Engineering, Universidade Federal Fluminense, Petrópolis, Brazil.
  • Siqueira-Batista R; Department of Medicine and Nursing, Universidade Federal de Viçosa, Viçosa, Brazil.
  • Bressan J; School of Medicine of the Faculdade Dinâmica do Vale do Piranga, Ponte Nova, Brazil.
Crit Rev Food Sci Nutr ; 63(7): 902-919, 2023.
Article em En | MEDLINE | ID: mdl-34323627
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Systematic_reviews Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Aprendizado de Máquina Tipo de estudo: Prognostic_studies / Systematic_reviews Idioma: En Ano de publicação: 2023 Tipo de documento: Article