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Classification Model of Food Groups in Food Exchange Table Using Decision Tree-based Machine Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(12), pp.51-58
  • DOI : 10.9708/jksci.2022.27.12.051
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 25, 2022
  • Accepted : December 14, 2022
  • Published : December 30, 2022

Ji Yun Kim 1 Jong Wan Kim 2

1더본코리아 롤링파스타
2삼육대학교

Accredited

ABSTRACT

In this paper, we propose a decision tree-based machine learning model that leads to food exchange table renewal by classifying food groups through machine learning for existing food and food data found by web crawling. The food exchange table is the standard for food exchange intake when composing a diet such as diet and diet, as well as patients who need nutritional management. The food exchange table, which is the standard for the composition of the diet, takes a lot of manpower and time in the process of revision through the National Health and Nutrition Survey, making it difficult to quickly reflect food changes according to new foods or trends. Since the proposed technique classifies newly added foods based on the existing food group, it is possible to organize a rapid food exchange table reflecting the trend of food. As a result of classifying food into the proposed model in the study, the accuracy of the food group in the food exchange table was 97.45%, so this food classification model is expected to be highly utilized for the composition of a diet that suits your taste in hospitals and nursing homes.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.