TY - GEN
T1 - Binary and multi-class parkinsonian disorders classification using support vector machines
AU - Morisi, Rita
AU - Gnecco, Giorgio
AU - Lanconelli, Nico
AU - Zanigni, Stefano
AU - Manners, David Neil
AU - Testa, Claudia
AU - Evangelisti, Stefania
AU - Gramegna, Laura Ludovica
AU - Bianchini, Claudio
AU - Cortelli, Pietro
AU - Tonon, Caterina
AU - Lodi, Raffaele
PY - 2015
Y1 - 2015
N2 - 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%.
AB - 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%.
KW - Binary classification
KW - Feature selection
KW - Multi–class classification
KW - Parkinsonian disorders classification
KW - Support VectorMachines
UR - http://www.scopus.com/inward/record.url?scp=84937433206&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937433206&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-19390-8_43
DO - 10.1007/978-3-319-19390-8_43
M3 - Conference contribution
AN - SCOPUS:84937433206
SN - 9783319193892
VL - 9117
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 379
EP - 386
BT - Pattern Recognition and Image Analysis - 7th Iberian Conference, IbPRIA 2015, Proceedings
PB - Springer Verlag
T2 - 7th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2015
Y2 - 17 June 2015 through 19 June 2015
ER -