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Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly

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Author(s)
Myonghwa ParkHyeyoung KimSun Kyung Kim
Keimyung Author(s)
Park, Myong HwaKim, Hye Young
Department
Dept. of Nursing (간호학)
Journal Title
Healthcare Informatics Research
Issued Date
2014
Volume
20
Issue
1
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.
Keywords: Decision Trees, Data Mining, Malnutrition, Aged, Community
Keimyung Author(s)(Kor)
박명화
김혜영
Publisher
College of Nursing
Citation
Myonghwa Park et al. (2014). Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly. Healthcare Informatics Research, 20(1), 30–38. doi: 10.4258/hir.2014.20.1.30
Type
Article
ISSN
2093-3681
DOI
10.4258/hir.2014.20.1.30
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/35805
Appears in Collections:
2. College of Nursing (간호대학) > Dept. of Nursing (간호학)
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