Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly / 대한의료정보학회지
Healthcare Informatics Research
; : 30-38, 2014.
Article
em En
| WPRIM
| ID: wpr-208936
Biblioteca responsável:
WPRO
ABSTRACT
OBJECTIVES: The purpose of this study was to design a prediction model that explains the characteristics of elderly adults at risk of malnutrition. METHODS: Data were obtained from a large data set, 2008 Korean Elderly Survey, in which the data of 15,146 subjects were entered. With nutritional status a target variable, the input variables included the demographic and socioeconomic status of participants. The data were analyzed by using the SPSS Clementine 12.0 program's feature selection node to select meaningful variables. RESULTS: Among the C5.0, C&R Tree, QUEST, and CHAID models, the highest predictability was reported by C&R Tree with the accuracy rate of 77.1%. The presence of more than two comorbidities, living alone status, having severe difficulty in daily activities, and lower perceived economic status were identified as risk factors of malnutrition in elderly. CONCLUSIONS: A reliable decision support model was designed to provide accurate information regarding the characteristics of elderly individuals with malnutrition. The findings demonstrated the good feasibility of data mining when used for a large community data set and its value in assisting health professionals and local decision makers to come up with effective strategies for achieving public health goals.
Palavras-chave
Texto completo:
1
Índice:
WPRIM
Assunto principal:
Classe Social
/
Árvores de Decisões
/
Comorbidade
/
Saúde Pública
/
Estado Nutricional
/
Fatores de Risco
/
Técnicas de Apoio para a Decisão
/
Desnutrição
/
Mineração de Dados
/
Conjunto de Dados
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Aged
/
Humans
Idioma:
En
Revista:
Healthcare Informatics Research
Ano de publicação:
2014
Tipo de documento:
Article