Analysis of risk factors and predictive model for recurrent falls in community dwelling older adults Online publication date: Tue, 01-Feb-2011
by Selen Onel, Abe Zeid, Sagar Kamarthi, Meredith Hinds Harris
International Journal of Collaborative Enterprise (IJCENT), Vol. 1, No. 3/4, 2010
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.
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