Clinical Diagnosis Based on Bayesian Classification of Functional Magnetic-Resonance Data

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-dimensiona...

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Published inNeuroinformatics (Totowa, N.J.) Vol. 5; no. 3; pp. 178 - 188
Main Authors Chen, Rong, Herskovits, Edward H.
Format Journal Article
LanguageEnglish
Published United States Springer Nature B.V 01.01.2007
Subjects
Algorithms
Bayes Theorem
Brain - blood supply
Brain - physiology
Brain Mapping
Humans
Image Processing, Computer-Assisted - methods
Learning - physiology
Magnetic Resonance Imaging
Studies
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Summary: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.
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ISSN:1539-2791
1559-0089
DOI:10.1007/s12021-007-0007-2