title: Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease.
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.
authors.forename: Piers
authors.initials: P
authors.lastname: Johnson
authors.forename: Luke
authors.initials: L
authors.lastname: Vandewater
authors.forename: William
authors.initials: W
authors.lastname: Wilson
authors.forename: Paul
authors.initials: P
authors.lastname: Maruff
authors.forename: Greg
authors.initials: G
authors.lastname: Savage
authors.forename: Petra
authors.initials: P
authors.lastname: Graham
authors.forename: Lance S
authors.initials: LS
authors.lastname: Macaulay
authors.forename: Kathryn A
authors.initials: KA
authors.lastname: Ellis
authors.forename: Cassandra
authors.initials: C
authors.lastname: Szoeke
authors.forename: Ralph N
authors.initials: RN
authors.lastname: Martins
authors.forename: Christopher C
authors.initials: CC
authors.lastname: Rowe
authors.forename: Colin L
authors.initials: CL
authors.lastname: Masters
authors.forename: David
authors.initials: D
authors.lastname: Ames
authors.forename: Ping
authors.initials: P
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000235:genetics; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000235:genetics; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D006801:Humans; D016015:Logistic Models; D012372:ROC Curve; D012307:Risk Factors