Brain Magnetic Resonance Spectroscopy Classifiers

  • Susana Oliveira
  • Jaime Rocha
  • Victor Alves
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)


During the last decade, the Magnetic Resonance Spectroscopy modality has become an integrant part of the diagnostic routine. However, the visual interpretation of these spectra is difficult and few clinicians are trained to use the technique. In this study, sixty-eight spectra obtained from twenty-two multi-voxel spectroscopies were classified using three well-known classification algorithms: K-Nearest Neighbors (KNN), Decision Trees and Naïve Bayes. The best results were obtained using NaïveBayes that presented an average balanced accuracy rate around 75%, although K-Nearest Neighbors presented very good results in some situations. The obtained results lead us to conclude that it is possible to classify magnetic resonance spectra with data mining techniques for further integration in a Clinical Decision Support System which may help in the diagnosis of new cases.


Feature Selection Magnetic Resonance Spectroscopy Linear Discriminant Analysis Clinical Decision Support System Data Mining Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Susana Oliveira
    • 1
  • Jaime Rocha
    • 2
  • Victor Alves
    • 1
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.Department of NeuroradiologyHospital de BragaPortugal

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