Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease

BMC Bioinformatics

Research

Greg Savage¹  Colin L Masters²  Kathryn A Ellis³  David Ames⁴  Christopher C Rowe⁵  Petra Graham⁶  Luke Vandewater⁷  Piers Johnson⁷  Ping Zhang⁸  William Wilson⁸  Lance S Macaulay⁹  Cassandra Szoeke^1,10  Ralph N Martins^1,11  Paul Maruff^1,12 

[1] ARC Centre of Excellence in Cognition and its Disorders, and Department of Psychology, Macquarie University, Sydney, Australia;ARC Centre of Excellence in Cognition and its Disorders, and Department of Psychology, Macquarie University, Sydney, Australia;Department of Pathology, The University of Melbourne, VIC, Australia;Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, VIC, Australia;Mental Health Research Institute, Melbourne, VIC, Australia;Academic Unit for Psychiatry of Old Age, Department of Psychiatry, The University of Melbourne, VIC, Australia;National Ageing Research Institute, Melbourne, VIC, Australia;Department of Nuclear Medicine & Centre for PET, Austin Health, Melbourne, VIC, Australia;Department of Medicine, University of Melbourne, VIC, Australia;Department of Statistics, Faculty of Science, Macquarie University, Sydney, NSW, Australia;Digital Productivity Flagship, CSIRO, Marsfield, NSW, Australia;Digital Productivity Flagship, CSIRO, Marsfield, NSW, Australia;CRC for Mental Health, Melbourne, VIC, Australia;Food and Nutrition Flagship, CSIRO, Parkville, VIC, Australia;Mental Health Research Institute, Melbourne, VIC, Australia;School of Exercise Biomedical and Health Sciences, Edith Cowan University, Perth, WA, Australia;Sir James McCusker Alzheimer's Disease Research Unit, Perth, WA, Australia;The Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia;CogState Ltd, Melbourne, VIC, Australia;

关键词: Genetic Algorithm;    Mild Cognitive Impairment;    Monte Carlo;    Clinical Dementia Rate;    Semantic Fluency;   

DOI  :  10.1186/1471-2105-15-S16-S11

来源: Springer

【 摘 要 】

BackgroundAssessment 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 poorly understood, as in the case in prediction of AD development.ResultsMultiple sets of neuropsychological tests were identified by GA to best predict conversions between clinical categories, with a cross validated AUC (area under the ROC curve) of 0.90 for prediction of HC conversion to MCI/AD and 0.86 for MCI conversion to AD within 36 months.ConclusionsThis study showed the potential of GA application in the neural science area. It demonstrated that the combination of a small set of variables is superior in performance than the use of all the single significant variables in the model for prediction of progression of disease. Variables more frequently selected by GA might be more important as part of the algorithm for prediction of disease development.

【 授权许可】

Unknown   
© Johnson et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.