title: Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease.
abstract: BACKGROUND
  The aim of this study was to build a random forest classifier to improve the diagnostic accuracy in differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and to quantify the relevance of multimodal diagnostic measures, with a focus on electroencephalography (EEG).
  
  
  METHODS
  A total of 66 DLB, 66 AD patients, and 66 controls were selected from the Amsterdam Dementia Cohort. Quantitative EEG (qEEG) measures were combined with clinical, neuropsychological, visual EEG, neuroimaging, and cerebrospinal fluid data. Variable importance scores were calculated per diagnostic variable.
  
  
  RESULTS
  For discrimination between DLB and AD, the diagnostic accuracy of the classifier was 87%. Beta power was identified as the single-most important discriminating variable. qEEG increased the accuracy of the other multimodal diagnostic data with almost 10%.
  
  
  CONCLUSIONS
  Quantitative EEG has a higher discriminating value than the combination of the other multimodal variables in the differentiation between DLB and AD.
authors.affiliation: Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Meenakshi
authors.initials: M
authors.lastname: Dauwan
authors.affiliation: Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Jessica J
authors.initials: JJ
authors.lastname: van der Zande
authors.affiliation: Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands; Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands; Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Edwin
authors.initials: E
authors.lastname: van Dellen
authors.affiliation: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands.
authors.forename: Iris E C
authors.initials: IE
authors.lastname: Sommer
authors.affiliation: Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Philip
authors.initials: P
authors.lastname: Scheltens
authors.affiliation: Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Afina W
authors.initials: AW
authors.lastname: Lemstra
authors.affiliation: Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
authors.forename: Cornelis J
authors.initials: CJ
authors.lastname: Stam
keywords: Alzheimer's disease; Beta power; Dementia with Lewy bodies; Diagnostic accuracy; EEG; Machine learning; Random forest