Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Neural Computing and Applications

DOI: 10.1007/s00521-026-12083-2

Authors: Nur Kucukaydin, Gokce Ozden-Gurcan, Ebru Turan-Kizildogan

Institution: Eskişehir Osmangazi University

Language: English

Topic: Balance, Gait, and Falls Prevention

Subfield: Physical Therapy, Sports Therapy and Rehabilitation

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $3,290