title: Family-wise automatic classification in schizophrenia.
abstract: Automatic classification of individuals at increased risk for schizophrenia can become an important screening method that allows for early intervention based on disease markers, if proven to be sufficiently accurate. Conventional classification methods typically consider information from single subjects, thereby ignoring (heritable) features of the person's relatives. In this paper we show that the inclusion of these features can lead to an increase in classification accuracy from 0.54 to 0.72 using a support vector machine model. This inclusion of contextual information is especially useful in diseases where the classification features carry a heritable component.
authors.affiliation: Department of Psychiatry, University Medical Center Utrecht, Rudolf Magnus Institute of Neuroscience, The Netherlands. r.mandl@umcutrecht.nl
authors.forename: René C W
authors.initials: RC
authors.lastname: Mandl
authors.forename: Rachel M
authors.initials: RM
authors.lastname: Brouwer
authors.forename: Wiepke
authors.initials: W
authors.lastname: Cahn
authors.forename: René S
authors.initials: RS
authors.lastname: Kahn
authors.forename: Hilleke E
authors.initials: HE
authors.lastname: Hulshoff Pol
mesh_terms: D000328:Adult; D005192:Family Health; D005260:Female; D006801:Humans; D008297:Male; D012559:Schizophrenia / Q000145:classification* / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D055815:Young Adult
keywords: Automatic classification; Contextual information; Early detection; Heritability; MRI; Support vector machine