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논문 기본 정보

자료유형
학술저널
저자정보
Seo-hyun Kim (Yonsei University Wonju) Chung-hwi Yi (Yonsei University Wonju) Jin-seok Lim (Yonsei University Wonju)
저널정보
한국전문물리치료학회 한국전문물리치료학회지 한국전문물리치료학회지 제28권 제3호
발행연도
2021.1
수록면
177 - 185 (9page)

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초록· 키워드

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Background: Muscle undergoes change continuously with aging. Sarcopenia, in which muscle mass decrease with aging, is associated with various diseases, the risk of falling, and the deterioration of quality of life. Obesity and sarcopenia also have a synergy effect on the disease of the older adults. Objects: This study examined the risk factors for sarcopenia, sarcopenic obesity, and sarcope-nia without obesity and developed prediction models. Methods: This machine-learning study used the 2008?2011 Korea National Health and Nu-trition Examination Surveys in the analysis. After data curation, 5,563 older participants were selected, of whom 1,169 had sarcopenia, 538 had sarcopenic obesity, and 631 had sarco-penia without obesity; the remaining 4,394 were normal. Decision tree and random forest models were used to identify risk factors. Results: The risk factors for sarcopenia chosen by both methods were body mass index (BMI) and duration of moderate physical activity; those for sarcopenic obesity were sex, BMI, and duration of moderate physical activity; and those for sarcopenia without obesity were BMI and sex. The areas under the receiver operating characteristic curves of all prediction models exceeded 0.75. BMI could predict sarcopenia-related disease. Conclusion: Risk factors for sarcopenia-related diseases should be identified and programs for sarcopenia-related disease prevention should be developed. Data-mining research using population data should be conducted to enhance the effectiveness of early treatment for people with sarcopenia-related diseases through predictive models.

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