Sarcopenia is becoming a severe global public health concern as the world's elderly population grows. Sarcopenia is characterized by muscular mass and strength loss, as well as impaired physical performance, and it is frequently connected with negative health outcomes such as falls. Falls are a primary cause of death in older individuals and must be addressed. Sarcopenia is currently diagnosed clinically using three primary technologies: imaging technology, precision medicine, and machine learning. In this study, the investigators will use previously collected data from nearly 200 community-dwelling subjects, including medical history, biochemistry, body composition, balance and gait, electromyography, and functional performance, to extract the characteristics and classification of sarcopenia using machine learning techniques such as empirical mode decomposition technology and decision tree algorithms. The investigators intend to offer clinically proven and effective interventional strategies to prevent, stabilize, and reverse sarcopenia.
Sarcopenia is quickly becoming a major global public health issue. Falls are the leading cause of mortality among the elderly, and they must be addressed. The investigators will use machine learning techniques such as empirical mode decomposition technology and decision tree algorithms to extract the characteristics and classification of sarcopenia in this retrospective study in order to offer clinically proven and effective interventional strategies to prevent, stabilize, and reverse sarcopenia.