The effect of reduction in cross-validation intervals on the performance of multifactor dimensionality reduction

Alison A. Motsinger, Marylyn D. Ritchie

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

Multifactor Dimensionality Reduction (MDR) was developed to detect genetic polymorphisms that present an increased risk of disease. Cross-validation (CV) is an important part of the MDR algorithm, as it prevents over-fitting and allows the predictive ability of a model to be evaluated. CV is a computationally intensive step in the MDR algorithm. Traditionally, MDR has been implemented using 10-fold CV In order to reduce computation time and therefore allow MDR analysis to be applied to larger datasets, we evaluated the possibility of eliminating or reducing the number of CV intervals used for analysis. We found that eliminating CV made final model selection impossible, but that reducing the number of CV intervals from ten to five caused no loss of power, thereby reducing the computation time of the algorithm by half. The validity of this reduction was confirmed with data from an Alzheimer's disease (AD) study.

Original languageEnglish (US)
Pages (from-to)546-555
Number of pages10
JournalGenetic Epidemiology
Volume30
Issue number6
DOIs
StatePublished - Sep 1 2006

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Genetics(clinical)

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