title: Improving Parkinson's disease identification through evolutionary-based feature selection.
abstract: Parkinson's disease (PD) automatic identification has been actively pursued over several works in the literature. In this paper, we deal with this problem by applying evolutionary-based techniques in order to find the subset of features that maximize the accuracy of the Optimum-Path Forest (OPF) classifier. The reason for the choice of this classifier relies on its fast training phase, given that each possible solution to be optimized is guided by the OPF accuracy. We also show results that improved other ones recently obtained in the context of PD automatic identification.
authors.affiliation: Institute of Physicsat São Carlos, University of São Paulo, São Carlos, Brazil. spadotto@gmail.com
authors.forename: André A
authors.initials: AA
authors.lastname: Spadoto
authors.forename: Rodrigo C
authors.initials: RC
authors.lastname: Guido
authors.forename: Felipe L
authors.initials: FL
authors.lastname: Carnevali
authors.forename: André F
authors.initials: AF
authors.lastname: Pagnin
authors.forename: Alexandre X
authors.initials: AX
authors.lastname: Falcão
authors.forename: João P
authors.initials: JP
authors.lastname: Papa
mesh_terms: D000465:Algorithms; D006801:Humans; D010300:Parkinson Disease / Q000175:diagnosis*