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International Journal of Telemedicine and Applications
Volume 2016 (2016), Article ID 6837498, 9 pages
Research Article

A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests

Department of CSE and IT, School of Electrical Engineering and Computer Science, Shiraz University, Shiraz 71348-51154, Iran

Received 10 November 2015; Accepted 27 March 2016

Academic Editor: Aura Ganz

Copyright © 2016 Mahnaz Behroozi and Ashkan Sami. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Recently, speech pattern analysis applications in building predictive telediagnosis and telemonitoring models for diagnosing Parkinson’s disease (PD) have attracted many researchers. For this purpose, several datasets of voice samples exist; the UCI dataset named “Parkinson Speech Dataset with Multiple Types of Sound Recordings” has a variety of vocal tests, which include sustained vowels, words, numbers, and short sentences compiled from a set of speaking exercises for healthy and people with Parkinson’s disease (PWP). Some researchers claim that summarizing the multiple recordings of each subject with the central tendency and dispersion metrics is an efficient strategy in building a predictive model for PD. However, they have overlooked the point that a PD patient may show more difficulty in pronouncing certain terms than the other terms. Thus, summarizing the vocal tests may lead into loss of valuable information. In order to address this issue, the classification setting must take what has been said into account. As a solution, we introduced a new framework that applies an independent classifier for each vocal test. The final classification result would be a majority vote from all of the classifiers. When our methodology comes with filter-based feature selection, it enhances classification accuracy up to 15%.