Abstract
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 poorly understood, as in the case in prediction of AD development. This 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.
RAS ID
18865
Document Type
Conference Proceeding
Date of Publication
1-1-2014
Faculty
Faculty of Health, Engineering and Science
School
School of Medical Sciences / Centre of Excellence for Alzheimer's Disease Research and Care
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
BMC Genomics
Included in
Bioinformatics Commons, Neuroscience and Neurobiology Commons, Theory and Algorithms Commons
Comments
Johnson, P., Vandewater, L., Wilson, W., Maruff, P., Savage, G., Graham, P., Macaulay, L., Ellis, K., Szoeke, C., Martins, R. N., Rowe, C., Masters, C., Ames, D., & Zhang, P. (2014). Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease. Proceedings of Asia-Pacific Bioinformatics Conference. (pp. 1 - 14). Sydney, NSW. BMC Genomics. Available here