Fall prediction in a quiet standing balance test via machine learning: Is it possible?

ABSTRACT: The elderly population is growing rapidly in the world and falls are becoming a big problem for society. Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. They are considered the gold standard to indicate optimal mobility and performance individually, but their sensitivity and specificity are not good enough to predict who is at higher risk of falling. An innovative approach for fall prediction is the machine learning. Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms in classify participants by age, number of falls and falls frequency based on features extracted from

SUBMITTER: Pennone J 

PROVIDER: S-EPMC11020412 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature