@incollection{eprints2776,
           pages = {379--386},
          volume = {9117},
          series = {Lecture Notes in Computer Science},
          author = {Rita Morisi and Giorgio Gnecco and Nico Lanconelli and Stefano Zanigni and David Neil Manners and Claudia Testa and Stefania Evangelisti and LauraLudovica Gramegna and Claudio Bianchini and Pietro Cortelli and Caterina Tonon and Raffaele Lodi},
            year = {2015},
       booktitle = {Pattern Recognition and Image Analysis},
           title = {Binary and Multi-class Parkinsonian Disorders Classification Using Support Vector Machines},
       publisher = {Springer},
             url = {http://eprints.imtlucca.it/2776/},
        keywords = {Support Vector Machines; Feature selection; Binary classification; Multi?class classification; Parkinsonian disorders classification},
        abstract = {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\%.}
}