Abstract
This paper presents a method for an automated Parkinsonian disorders classification using Support Vector Machines (SVMs). Magnetic Resonance quantitative markers are used as features to train SVMs with the aim of automatically diagnosing patients with different Parkinsonian disorders. Binary and multi–class classification problems are investigated and applied with the aim of automatically distinguishing the subjects with different forms of disorders. A ranking feature selection method is also used as a preprocessing step in order to asses the significance of the different features in diagnosing Parkinsonian disorders. In particular, it turns out that the features selected as the most meaningful ones reflect the opinions of the clinicians as the most important markers in the diagnosis of these disorders. Concerning the results achieved in the classification phase, they are promising; in the two multi–class classification problems investigated, an average accuracy of \(81\,\%\) and \(90\,\%\) is obtained, while in the binary scenarios taken in consideration, the accuracy is never less than \(88\,\%\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sharma, S., et al.: Biomarkers in Parkinson’s disease (recent update). Neurochem. Int. 63(3), 201–229 (2013)
Cherubini, A., et al.: Magnetic resonance support vector machine discriminates between parkinson disease and progressive supranuclear palsy. Mov. Disord. 29(2), 266–269 (2014)
Long, D., et al.: Automatic classification of early Parkinson’s disease with multi-modal MR imaging. PloS one 7(11), e47714 (2012)
Haller, S., et al.: Individual detection of patients with Parkinson disease using support vector machine analysis of diffusion tensor imaging data: initial results. Am. J. Neuroradiol. 33(11), 2123–2128 (2012)
Marquand, A.F., et al.: Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach. PloS one 8(7), e69237 (2013)
Gelb, D.J., Oliver, E., Gilman, S.: Diagnostic criteria for Parkinson disease. Arch. Neurol. 56(1), 33–39 (1999)
Rizzo, G., et al.: Diffusion-weighted brain imaging study of patients with clinical diagnosis of corticobasal degeneration, progressive supranuclear palsy and parkinson’s disease. Brain 131(10), 2690–2700 (2008)
Ben-Hur, A., Jason, W.: A user’s guide to support vector machines. In: Carugo, O., Eisenhaber, F. (eds.) Data Mining Techniques for the Life Sciences, pp. 223–239. Humana Press, New York (2010)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)
Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Smola, A.J., Bartlett, P.L., Scholkopf, B., Schurmans, D. (eds.) Advances in Large Margin Classifiers, pp. 61–74. MIT Press, Cambridge (1999)
Battiti, R.: Using mutual information for selecting features in supervised neural net learning. IEEE Trans. Neural Netw. 5(4), 537–550 (1994)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, New York (2009)
Abdel-Aal, R.E.: GMDH-based feature ranking and selection for improved classification of medical data. J. Biomed. Inform. 38(6), 456–468 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Morisi, R. et al. (2015). Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_43
Download citation
DOI: https://doi.org/10.1007/978-3-319-19390-8_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19389-2
Online ISBN: 978-3-319-19390-8
eBook Packages: Computer ScienceComputer Science (R0)