IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-03-29T01:47:00ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2016-02-26T15:47:40Z2016-03-01T10:41:10Zhttp://eprints.imtlucca.it/id/eprint/3145This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/31452016-02-26T15:47:40ZBinary and
multi-class Parkinsonian disorders classification using Support Vector Machines with
graph-based featuresRita Morisirita.morisi@imtlucca.itGiorgio Gneccogiorgio.gnecco@imtlucca.itNico LanconelliStefano ZanigniDavid Neil MannersClaudia TestaStefania EvangelistiLaura Ludovica GramegnaClaudio BianchiniPietro CortelliCaterina TononRaffaele Lodi2016-02-26T14:35:46Z2016-02-29T08:31:00Zhttp://eprints.imtlucca.it/id/eprint/3129This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/31292016-02-26T14:35:46ZBinary and multi-class classification of parkinsonian disorders with support vector machines based on quantitative brain MR and graph-based featuresLaura Ludovica GramegnaClaudia TestaRita Morisirita.morisi@imtlucca.itStefano ZanigniGiorgio Gneccogiorgio.gnecco@imtlucca.itNico LanconelliDavid Neil MannersStefania EvangelistiPietro CortelliCaterina TononRaffaele Lodi2015-10-19T09:40:53Z2016-04-06T08:50:40Zhttp://eprints.imtlucca.it/id/eprint/2776This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/27762015-10-19T09:40:53ZBinary and Multi-class Parkinsonian Disorders Classification Using Support Vector MachinesThis 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%.Rita Morisirita.morisi@imtlucca.itGiorgio Gneccogiorgio.gnecco@imtlucca.itNico LanconelliStefano ZanigniDavid Neil MannersClaudia TestaStefania EvangelistiLauraLudovica GramegnaClaudio BianchiniPietro CortelliCaterina TononRaffaele Lodi