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Korean J Community Nutr. 1999 Sep;4(3):421-430. Korean. Original Article.
Lee SY , Paik HY , Yoo SM .
Department of Home Economics education, College of Education, Dongguk University, Seoul, Korea.
Department of Food and Nutritioin, Seoul, Korea.
College of Mechanical and Industrial, Sytem Engineering, Kyunghee University, Seoul, Korea.
Abstract

A neural network system was applied in order to analyze the nutritional and other factors influencing chronic diseases. Five different nutrition evaluation methods including SD Score, %RDA, NAR INQ and %RDA-SD Score were utilized to facilitate nutrient data for the system. Observing top three chronic disease prediction ratio, WHR using SD Score was the most frequently quoted factor revealing the highest predication rate as 62.0%. Other high prediction rates using other data processing methods are as follows. Prediction rate with %RDA, NAR, INQ and %RDA-SD Score were 58.5%(diabetes), 53.5%(hyperlipidemia), 51.6%(diabetes), and 58.0%(diabetes)respectively. Higher prediction rate was observed using either NAR or INQ for obesity as 51.7% and 50.9% compared to the previous result using SD Score. After reviewing appearance rate for all chronic disease and for various data processing method used, it was found that iron and vitamin C were the most frequently cited factors resulting in high prediction rate.

Copyright © 2019. Korean Association of Medical Journal Editors.