Fall risk prediction in elderly nursing home residents using artificial neural networks and berg balance data

One of the most important problems faced by elderly individuals is the risk of falling. This risk is a leading cause of hospitalization among older adults and significantly increases healthcare costs. Due to the increasing elderly population, the provision of health services and the assessment of fall risk in this population have become increasingly important. The growing use of artificial intelligence in healthcare has highlighted the importance of studies focused on health services for the elderly. This study investigated the effect of circuit exercises on balance in the elderly. An ANN model was developed to investigate the effects of questions regarding the characteristics and demographic features of the elderly and of the Berg Balance Scale. The model estimates Performance-Oriented Mobility Assessment test scores to determine fall risk in this population. When results obtained from the test parameters are compared with experimental findings, the designed model demonstrates effective performance in predicting fall risk.