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

被引：4

作者：

Morisi, Rita ^{[1
]}

Gnecco, Giorgio ^{[1
]}

Lanconelli, Nico ^{[2
]}

Zanigni, Stefano ^{[3
]}

Manners, David Neil ^{[3
]}

Testa, Claudia ^{[3
]}

Evangelisti, Stefania ^{[3
]}

Gramegna, Laura Ludovica ^{[3
]}

Bianchini, Claudio ^{[3
]}

Cortelli, Pietro ^{[3
]}

Tonon, Caterina ^{[3
]}

Lodi, Raffaele ^{[3
]}

机构：

[1] IMT Inst Adv Studies, I-55100 Lucca, Italy

[2] Univ Bologna, Alma Mater Studiorum, Dipartimento Fis & Astron, I-40127 Bologna, Italy

[3] Univ Bologna, Dept Biomed & NeuroMotor Sci, Policlin S Orsola Malpighi, Funct MR Unit, Bologna, Italy

来源：

PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015) | 2015年 / 9117卷

关键词：

Support Vector Machines; Feature selection; Binary classification; Multi class classification; Parkinsonian disorders classification; DISEASE;

D O I：

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

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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%.

引用

页码：379 / 386

页数：8

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