Binary and multi-class parkinsonian disorders classification using support vector machines

Rita Morisi, Giorgio Gnecco, Nico Lanconelli, Stefano Zanigni, David Neil Manners, Claudia Testa, Stefania Evangelisti, Laura Ludovica Gramegna, Claudio Bianchini, Pietro Cortelli, Caterina Tonon, Raffaele Lodi

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

Abstract

This paper presents a method for an automated Parkinsonian disorders classification using Support VectorMachines (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.Aranking feature selectionmethod 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%.

Original languageEnglish
Title of host publicationPattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings
PublisherSpringer Verlag
Pages379-386
Number of pages8
Volume9117
ISBN (Print)9783319193892
DOIs
Publication statusPublished - 2015
Event7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015 - Santiago de Compostela, Spain
Duration: Jun 17 2015Jun 19 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9117
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015
CountrySpain
CitySantiago de Compostela
Period6/17/156/19/15

Keywords

  • Binary classification
  • Feature selection
  • Multi–class classification
  • Parkinsonian disorders classification
  • Support VectorMachines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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