title: Multimodal prediction of conversion to Alzheimer's disease based on incomplete biomarkers.
abstract: BACKGROUND
  This study investigates the prediction of mild cognitive impairment-to-Alzheimer's disease (MCI-to-AD) conversion based on extensive multimodal data with varying degrees of missing values.
  
  
  METHODS
  Based on Alzheimer's Disease Neuroimaging Initiative data from MCI-patients including all available modalities, we predicted the conversion to AD within 3 years. Different ways of replacing missing data in combination with different classification algorithms are compared. The performance was evaluated on features prioritized by experts and automatically selected features.
  
  
  RESULTS
  The conversion to AD could be predicted with a maximal accuracy of 73% using support vector machines and features chosen by experts. Among data modalities, neuropsychological, magnetic resonance imaging, and positron emission tomography data were most informative. The best single feature was the functional activities questionnaire.
  
  
  CONCLUSIONS
  Extensive multimodal and incomplete data can be adequately handled by a combination of missing data substitution, feature selection, and classification.
authors.affiliation: Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany.
authors.forename: Kerstin
authors.initials: K
authors.lastname: Ritter
authors.affiliation: Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany.
authors.forename: Julia
authors.initials: J
authors.lastname: Schumacher
authors.affiliation: Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany.
authors.forename: Martin
authors.initials: M
authors.lastname: Weygandt
authors.affiliation: Department of Nuclear Medicine, Charité - University Medicine Berlin, Berlin, Germany.
authors.forename: Ralph
authors.initials: R
authors.lastname: Buchert
authors.affiliation: Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany.
authors.forename: Carsten
authors.initials: C
authors.lastname: Allefeld
authors.affiliation: Berlin Center for Advanced Neuroimaging, Bernstein Center for Computational Neuroscience, Charité - University Medicine Berlin, Berlin, Germany.
authors.forename: John-Dylan
authors.initials: JD
authors.lastname: Haynes
keywords: Alzheimer's dementia; Feature selection; Mild cognitive impairment; Missing data; Multimodal biomarker; Prognosis