A neural network approach to sarcopenia prediction based on bioelectrical impedance in community-dwelling older adults.

ABSTRACT: This study aimed to apply a neural network to raw bioelectrical impedance analysis data and to test whether sarcopenia could be predicted with high accuracy. The study population comprised 727 community-dwelling older adults aged 65-85 years who participated in the Kasama Study from 2015 to 2018. Sarcopenia was determined using the standard values set by the Asian Working Group for Sarcopenia 2019. Skeletal muscle mass index, grip strength, and five-times sit-to-stand test (Dataset 1) or skeletal muscle mass index, grip strength, and gait speed (Dataset 2) were used. The characteristic variables were sex, age, height, and body mass index, as well as parameters from bioelectrical impedance analysis, such as reactance, resistance, and impedance for six frequencies (1, 5, 50, 250, 500, and 10

SUBMITTER: Shibuya K 

PROVIDER: S-EPMC12582432 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature