Document Type

Conference Proceeding

Publisher

BMC Genomics

Faculty

Faculty of Health, Engineering and Science

School

School of Medical Sciences/Centre of Excellence for Alzheimer's Disease Research and Care

RAS ID

18865

Comments

This article was originally published as: 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. Original article available here

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.

DOI

10.1186/1471-2105-15-S16-S11

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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