Analysis of Dietary Factors of Chronic Disease Using a Neural Network / 대한지역사회영양학회지
Korean Journal of Community Nutrition
;
: 421-430, 1999.
Artigo
em Coreano
| WPRIM
| ID: wpr-106078
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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Ácido Ascórbico
/
Doença Crônica
/
Ferro
/
Obesidade
Tipo de estudo:
Estudo prognóstico
Idioma:
Coreano
Revista:
Korean Journal of Community Nutrition
Ano de publicação:
1999
Tipo de documento:
Artigo
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