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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Knowledge Discovery in a Community Data Set: Malnutrition among the Elderly
Myonghwa Park1, Hyeyoung Kim2, Sun Kyung Kim1
1College of Nursing, Chungnam National University, Daejeon, Korea.
Healthcare Informatics Research
|March 15, 2014
Summary
Malnutrition risk in elderly adults is predictable. Key factors include multiple comorbidities, living alone, daily activity difficulties, and low economic status, identified using a data mining model.
Area of Science:
- Gerontology
- Public Health
- Data Mining
Background:
- Malnutrition is a significant health concern among elderly populations.
- Identifying at-risk individuals is crucial for timely intervention and improved health outcomes.
Purpose of the Study:
- To develop a predictive model for identifying elderly adults at risk of malnutrition.
- To understand the key demographic and socioeconomic characteristics associated with malnutrition in older adults.
Main Methods:
- Utilized data from the 2008 Korean Elderly Survey (15,146 participants).
- Employed data mining techniques, specifically feature selection using SPSS Clementine, to identify significant risk factors.
- Evaluated multiple predictive models, including C5.0, C&R Tree, QUEST, and CHAID.
Main Results:
- The C&R Tree model demonstrated the highest predictability with an accuracy of 77.1%.
- Identified risk factors for malnutrition include: presence of more than two comorbidities, living alone, severe difficulty with daily activities, and lower perceived economic status.
Conclusions:
- A reliable decision support model was successfully designed to characterize elderly individuals at risk of malnutrition.
- Data mining is effective for analyzing large community datasets, aiding health professionals and policymakers in public health initiatives.
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