Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.

Falls play a critical role in older people’s life as it is an important source of morbidity and mortality in elders. In this article, elders fall risk is predicted based on a physiological profile approach using a multilayer neural network with back-propagation learning algorithm. The personal physiological profile of 200 elders was collected through a questionnaire and used as the experimental data for learning and testing the neural network. The profile contains a series of simple factors putting elders at risk for falls such as vision abilities, muscle forces, and some other daily activities and grouped into two sets: psychological factors and public factors. The experimental data were investigated to select factors with high impact using principal component analysis. The experimental results show an accuracy of ≈90 percent and ≈87.5 percent for fall prediction among the psychological and public factors, respectively.

Furthermore, combining these two datasets yield an accuracy of ≈91 percent that is better than the accuracy of single datasets. The proposed method suggests a set of valid and reliable measurements that can be employed in a range of health care systems and physical therapy to distinguish people who are at risk for falls.

Global Health Course

Global Health is fast becoming a priority of all health organisations. Get ahead of the curve, take part in this course and help improve health around the world.
Scott BuxtonResearch article posted by: Scott Buxton

My name is Scott and I am currently the editor of physiospot.

Away from the keyboard I am extended scope physiotherapist working in ED and an acute frailty unit specialising in rapid assessment and discharge of acutely unwell frail older people.

Speak Your Mind

*