Journal article
Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease
P Johnson, L Vandewater, W Wilson, P Maruff, G Savage, P Graham, LS Macaulay, KA Ellis, C Szoeke, RN Martins, CC Rowe, CL Masters, D Ames, P Zhang
BMC Bioinformatics | Published : 2014
Abstract
Background: Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorl..
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Funding Acknowledgements
We thank all the participants who took part in this study and the clinicians who referred participants. The AIBL study (www.AIBL.csiro.au) is a collaboration between CSIRO, Edith Cowan University (ECU), The Florey Institute of Neuroscience and Mental Health,), National Ageing Research Institute (NARI) and Austin Health. It also involves support from CogState Ltd., Hollywood Private Hospital, and Sir Charles Gairdner Hospital. The study received funding support from CSIRO, the Science and Industry Endowment Fund (www.SIEF.org.au), NHMRC and Dementia Collaborative Research Centres (DCRC), Alzheimer's Australia (AA) Alzheimer's Association and the McCusker Alzheimer's Research Foundation as well as Industry, including Pfizer, Merck, Janssen and GE Healthcare.