Journals of Gerontology Series A: Biological Sciences and Medical Sciences, Vol 55, Issue 1 M17-M21, Copyright © 2000 by The Gerontological Society of America
New approach to risk determination: development of risk profile for new falls among community-dwelling older people by use of a Genetic Algorithm Neural Network (GANN)
PA Bath, N Pendleton, K Morgan, JE Clague, MA Horan and SB Lucas
Sheffield Institute for Studies on Ageing, University of Sheffield, England. [email protected]
BACKGROUND: Falls risk in older people is multifactorial and complex. There
is uncertainty about the importance of specific risk factors. Genetic
algorithm neural networks (GANNs) can examine all available data and select
the best nonlinear combination of variables for predicting falls. The aim
of this work was to develop a risk profile for operationally defined new
falls in a random sample of older people by use of a GANN approach.
METHODS: A random sample of 1042 community- dwelling people aged 65 and
older, living in Nottingham, England, were interviewed at baseline (1985)
and survivors reinterviewed at a 4-year follow-up (n = 690). The at-risk
group (n = 435) was defined as those survivors who had not fallen in the
year before the baseline interview. A GANN was used to examine all
available attributes and, from these, to select the best nonlinear
combination of variables that predicted those people who fell 4 years
later. RESULTS: The GANN selected a combination of 16 from a potential 253
variables and correctly predicted 35/114 new fallers (sensitivity = 31%;
positive predictive value = 57%) and 295/321 nonfallers (specificity = 92%;
negative predictive value = 79%); total correct = 76%. The variables
selected by the GANN related to personal health, opportunity, and personal
circumstances. CONCLUSIONS: This study demonstrates the capacity of GANNs
to examine all available data and then to identify the best 16 variables
for predicting falls. The risk profile complements risk factors in the
current literature identified by use of standard and conventional
statistical methods. Additional data about environmental factors might
enhance the sensitivity of the GANN approach and help identify those older
people who are at risk of falling.
