@article{ref1,
title="Elderly fall risk prediction based on a physiological profile approach using artificial neural networks",
journal="Health informatics journal",
year="2016",
author="Razmara, Jafar and Zaboli, Mohammad Hassan",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="1460-4582",
doi="10.1177/1460458216677841",
url="http://dx.doi.org/10.1177/1460458216677841"
}