TY  - CHAP
N2  - 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%.
KW  - Support Vector Machines; Feature selection; Binary classification; Multi?class classification; Parkinsonian disorders classification
T3  - Lecture Notes in Computer Science
PB  - Springer
T2  - Pattern Recognition and Image Analysis
EP  - 386
ID  - eprints2776
SP  - 379
M1  - 9117
SN  - 978-3-319-19389-2
AV  - none
UR  - http://dx.doi.org/10.1007/978-3-319-19390-8_43
A1  - Morisi, Rita
A1  - Gnecco, Giorgio
A1  - Lanconelli, Nico
A1  - Zanigni, Stefano
A1  - Manners, David Neil
A1  - Testa, Claudia
A1  - Evangelisti, Stefania
A1  - Gramegna, LauraLudovica
A1  - Bianchini, Claudio
A1  - Cortelli, Pietro
A1  - Tonon, Caterina
A1  - Lodi, Raffaele
TI  - Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines
Y1  - 2015///
ER  -