Title: Analysis of risk factors and predictive model for recurrent falls in community dwelling older adults

Authors: Selen Onel, Abe Zeid, Sagar Kamarthi, Meredith Hinds Harris

Addresses: Department of Industrial and Mechanical Engineering, 230 Snell Engineering Center, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA. ' Department of Industrial and Mechanical Engineering, 230 Snell Engineering Center, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA. ' Department of Industrial and Mechanical Engineering, 230 Snell Engineering Center, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA. ' Department of Physical Therapy, 6D Robinson Hall, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA

Abstract: The objective of this study is to identify the significant risk factors of |fall| among elderly people living in community centres. Different factors that may associate with falls are analysed via logistic regression (LR) and artificial neural networks (ANNs) models. According to the analysis, the most significant predictor in LR analysis is found to be the variable |lost balance within the past year|. Other significant predictors are stroke, Hx of dizziness/fainting, fall within past 30 days, and the number of supplements/vitamins used. The LR model gives a prediction accuracy of 76.6% while an ANN model gives 73%. ANN is expected to provide a more robust computational model than LR to analyse past falls and predict future ones. However our research results are contrary to those expectations. These results suggest that the ANN model need a larger sample size to reach its full potential and to give more accurate predictions.

Keywords: elderly people; old people; aged people; age; risk factors; logistic regression; artificial neural networks; ANNs; predictive models; recurrent falls; communal dwellings; older adults; community centres; significant predictors; factor analysis; strokes; dizziness; fainting; supplements; vitamins; computational models; sample sizes; Hx; medical history; collaborative enterprises; collaboration; healthcare systems; systems engineering.

DOI: 10.1504/IJCENT.2010.038358

International Journal of Collaborative Enterprise, 2010 Vol.1 No.3/4, pp.359 - 380

Published online: 01 Feb 2011 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article