title: Clinical diagnosis based on bayesian classification of functional magnetic-resonance data.
abstract: We describe a method for classifying subjects based on functional magnetic-resonance (fMR) data, using a method combining a Bayesian-network classifier with inverse-tree structure (BNCIT), and ensemble learning. The central challenge is to generate a classifier from a small sample of high-dimensional data. The principal strengths of our method include the nonparametric multivariate Bayesian-network representation, and joint performance of feature selection and classification. Preliminary results indicate that this method can detect regions characterizing group differences, and can, on the basis of activation levels in these regions, accurately classify new subjects.
authors.affiliation: Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA. rong.chen@uphs.upenn.edu
authors.forename: Rong
authors.initials: R
authors.lastname: Chen
authors.forename: Edward H
authors.initials: EH
authors.lastname: Herskovits
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000098:blood supply* / Q000502:physiology; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007858:Learning / Q000502:physiology; D008279:Magnetic Resonance Imaging