Predictive model development for possible sarcopenia in community-dwelling older adults: a cross-sectional machine learning approach using the Korean frailty and aging cohort study.
Abstract
Sarcopenia, an age-related decline in muscle mass and physical function, is a major risk factor for frailty, a condition associated with negative health outcomes and increased disease burden in older adults. Providing simple, accurate community-based screening is essential for early prevention and improving the health and quality of life of older adults. Employing screening criteria for possible sarcopenia can broadly identify individuals at risk for sarcopenia and enhance early diagnosis and preventive measures. However, there is a lack of possible sarcopenia prediction models. This study developed and evaluated a model for predicting possible sarcopenia among community-dwelling older adults and identified key predictors.
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Research summary
The paper claims to develop a predictive model for identifying possible sarcopenia in older adults. The study is relevant as it addresses a significant age-related condition that impacts the health and quality of life of older adults, contributing to the understanding of frailty and potential preventive measures.
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