Parkinson's disease identification through Optimum-Path Forest
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Data
2010-12-01
Autores
Spadoto, André A.
Guido, Rodrigo C.
Papa, João Paulo [UNESP]
Falcão, Alexandre X.
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Resumo
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. © 2010 IEEE.
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Artificial intelligence techniques, Artificial Neural Network, Automatic recognition, Commonly used, Feature space, Kernel mapping, Parkinson's disease, Pattern recognition techniques, PD identification, Supervised classification, Diseases, Pattern recognition, Neural networks
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2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10, p. 6087-6090.