J. Biomedical Science and Engineering, 2014, 7, 147-156 Published Online March 2014 in SciRes. http://www.scirp.org/journal/jbise http://dx.doi.org/10.4236/jbise.2014.74019 How to cite this paper: Shahbakhi, M., Far, D.T. and Tahami, E. (2014) Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine. J. Biomedical Science and Engineering, 7, 147-156. http://dx.doi.org/10.4236/jbise.2014.74019 Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine Mohammad Shahbakhi 1* , Danial Taheri Far 1 , Ehsan Tahami 2 1 Department of Biomedical Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran 2 Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Email: * shahbakhti_m@yahoo.com Received 17 January 2014; revised 17 February 2014; accepted 24 February 2014 Copyright © 2014 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting opti- mized features from all extracted features. Afterwards a network based on support vector ma- chine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkin- son’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, op- timized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best clas- sification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5). Keywords Parkinson’s Disease; Speech Analysis; Genetic Algorithm; Support Vector Machine * Corresponding author.