Diagnosis of rheumatoid arthritis with optical tomography: Comparison of classification methods

Ludguier D. Montejo, Julio D. Montejo, Hyun Keol Kim, Uwe J. Netz, Christian D. Klose, Sabine Blaschke, P. A. Zwaka, Gerhard A. Müller, Jürgen Beuthan, Andreas Hielscher

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Linear discriminant analysis (LDA) and support vector machines (SVM) are used to classify reconstructed absorption coefficient distributions of the proximal interphalangeal joints as affected or not affected by rheumatoid arthritis. The performance of each classification method is quantified using the leave-n-out method. LDA is shown to yield high sensitivities, while SVM yields high specificities.
    Original languageEnglish (US)
    Title of host publicationProceedings of the 2010 IEEE 36th Annual Northeast Bioengineering Conference (NEBEC)
    StatePublished - 2010

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