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

doi:10.1007/978-3-319-19390-8_43

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

Istituto Scienze Neurologiche

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings
Publisher	Springer Verlag
Pages	379-386
Number of pages	8
Volume	9117
ISBN (Print)	9783319193892
DOIs	https://doi.org/10.1007/978-3-319-19390-8_43
Publication status	Published - 2015
Event	7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015 - Santiago de Compostela, Spain Duration: Jun 17 2015 → Jun 19 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9117
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015
Country/Territory	Spain
City	Santiago de Compostela
Period	6/17/15 → 6/19/15

Keywords

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

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-319-19390-8_43

Cite this

Morisi, R., Gnecco, G., Lanconelli, N., Zanigni, S., Manners, D. N., Testa, C., Evangelisti, S., Gramegna, L. L., Bianchini, C., Cortelli, P., Tonon, C., & Lodi, R. (2015). Binary and multi-class parkinsonian disorders classification using support vector machines. In Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings (Vol. 9117, pp. 379-386). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9117). Springer Verlag. https://doi.org/10.1007/978-3-319-19390-8_43

Binary and multi-class parkinsonian disorders classification using support vector machines. / Morisi, Rita; Gnecco, Giorgio; Lanconelli, Nico; Zanigni, Stefano; Manners, David Neil; Testa, Claudia; Evangelisti, Stefania; Gramegna, Laura Ludovica; Bianchini, Claudio; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele.

Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings. Vol. 9117 Springer Verlag, 2015. p. 379-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9117).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Morisi, R, Gnecco, G, Lanconelli, N, Zanigni, S, Manners, DN, Testa, C, Evangelisti, S, Gramegna, LL, Bianchini, C, Cortelli, P, Tonon, C & Lodi, R 2015, Binary and multi-class parkinsonian disorders classification using support vector machines. in Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings. vol. 9117, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9117, Springer Verlag, pp. 379-386, 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015, Santiago de Compostela, Spain, 6/17/15. https://doi.org/10.1007/978-3-319-19390-8_43

Morisi R, Gnecco G, Lanconelli N, Zanigni S, Manners DN, Testa C et al. Binary and multi-class parkinsonian disorders classification using support vector machines. In Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings. Vol. 9117. Springer Verlag. 2015. p. 379-386. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-19390-8_43

Morisi, Rita ; Gnecco, Giorgio ; Lanconelli, Nico ; Zanigni, Stefano ; Manners, David Neil ; Testa, Claudia ; Evangelisti, Stefania ; Gramegna, Laura Ludovica ; Bianchini, Claudio ; Cortelli, Pietro ; Tonon, Caterina ; Lodi, Raffaele. / Binary and multi-class parkinsonian disorders classification using support vector machines. Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings. Vol. 9117 Springer Verlag, 2015. pp. 379-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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