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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-12-08

AUTHORS

Piers Johnson, Luke Vandewater, William Wilson, Paul Maruff, Greg Savage, Petra Graham, Lance S Macaulay, Kathryn A Ellis, Cassandra Szoeke, Ralph N Martins, Christopher C Rowe, Colin L Masters, David Ames, Ping Zhang

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 poorly understood, as in the case in prediction of AD development. RESULTS: Multiple 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. CONCLUSIONS: 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. More... »

PAGES

s11-s11

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2105-15-s16-s11

DOI

http://dx.doi.org/10.1186/1471-2105-15-s16-s11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037145151

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/25521394


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