C-Support Vector Classification the Estimation of the MS Subgroups Classification with Selected Kernels and Parameters

Yeliz Karaca, Şengül Hayta, Rana Karabudak


The study has classified the subgroups of Multiple Sclerosis using Support
Vector Machines. C-SVC algorithm, one of the SVM
classifiers of multi class, has been utilized for the classification of MS
subgroups. For this purpose, 120 MS patient and 19 healthy individuals have been
included in our study. Through Magnetic Resonance Imaging (MRI), the number of
lesion diameter and Expanded Disability Status Scale data are applied through C-
Support Vector Classifier (C-SVC). By applying the data onto Radial Basis
Funrtion kernel, Polynomial kernel, Sigmoid kernel and Linear kernel, four of the
kernel type of C-SVC algorithm, the accuracy rates of MS subgroups
classification and the computation time during the training procedure are
computed and compared. Having applied C- Support Vector Classifier on MS
subgroups, classification achievement of Healthy individual and MS subgroups,
namely that of RRMS, SPMS end PPMS has been measured.


C-SVC Kernels, MS Subgroups, MRI, EDSS, Classification

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