title: Parkinson's disease identification through optimum-path forest.
abstract: Artificial intelligence techniques have been extensively used for the identification of several disorders related with the voice signal analysis, such as Parkinson's disease (PD). However, some of these techniques flaw by assuming some separability in the original feature space or even so in the one induced by a kernel mapping. In this paper we propose the PD automatic recognition by means of Optimum-Path Forest (OPF), which is a new recently developed pattern recognition technique that does not assume any shape/separability of the classes/feature space. The experiments showed that OPF outperformed Support Vector Machines, Artificial Neural Networks and other commonly used supervised classification techniques for PD identification.
authors.affiliation: Institute of Physics at São Carlos, University of São Paulo, São Carlos, Brazil. spadotto@gmail.com
authors.forename: Andre A
authors.initials: AA
authors.lastname: Spadoto
authors.forename: Rodrigo C
authors.initials: RC
authors.lastname: Guido
authors.forename: Joao P
authors.initials: JP
authors.lastname: Papa
authors.forename: Alexandre X
authors.initials: AX
authors.lastname: Falcao
mesh_terms: D000465:Algorithms; D006801:Humans; D010300:Parkinson Disease / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors; D014831:Voice