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Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines

Morisi, Rita and Gnecco, Giorgio and Lanconelli, Nico and Zanigni, Stefano and Manners, David Neil and Testa, Claudia and Evangelisti, Stefania and Gramegna, LauraLudovica and Bianchini, Claudio and Cortelli, Pietro and Tonon, Caterina and Lodi, Raffaele Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines. In: Pattern Recognition and Image Analysis. Lecture Notes in Computer Science, 9117 . Springer, pp. 379-386. ISBN 978-3-319-19389-2 (2015)

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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%.

Item Type: Book Section
Uncontrolled Keywords: Support Vector Machines; Feature selection; Binary classification; Multi–class classification; Parkinsonian disorders classification
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > RZ Other systems of medicine
T Technology > T Technology (General)
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 19 Oct 2015 09:40
Last Modified: 06 Apr 2016 08:50
URI: http://eprints.imtlucca.it/id/eprint/2776

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