title: Elderly fall risk prediction based on a physiological profile approach using artificial neural networks.
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.
authors.affiliation: University of Tabriz, Iran.
authors.forename: Jafar
authors.initials: J
authors.lastname: Razmara
authors.affiliation: University of Tabriz, Iran.
authors.forename: Mohammad Hassan
authors.initials: MH
authors.lastname: Zaboli
authors.affiliation: Tabriz University of Medical Sciences, Iran.
authors.forename: Hadi
authors.initials: H
authors.lastname: Hassankhani
mesh_terms: D000058:Accidental Falls / Q000706:statistics & numerical data*; D000203:Activities of Daily Living; D000368:Aged; D000465:Algorithms; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D012307:Risk Factors; D011795:Surveys and Questionnaires
keywords: elderly; fall risk prediction; neural networks; physiological profile