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)

Background. Falls risk in older people is multifactorial and complex. There is uncertainty about the importance of specific risk factors. Genetic algo rithm neural networks (GANNs) can examine all available data and select the best nonlinear combination of variables for predicting falls. The aim of t his 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 olde r, living in Nottingham, England, were interviewed at baseline (1985) and s urvivors 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 befo re the baseline interview. A GANN was used to examine all available attribu tes 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 variabl es 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 th e GANN related to personal health, opportunity, and personal circumstances. Conclusions. This study demonstrates the capacity of GANNs to examine all a vailable data and then to identify the best 16 variables for predicting fal ls. The risk profile complements risk factors in the current literature ide ntified 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.