Diagnosis of Parkinson’s Disease in Human Using Voice Signals

A full investigation into the features extracted from voice signals of people with and without Parkinson’s disease was performed. A total of 31 people with and without the disease participated in the data collection phase. Their voice signals were recorded and processed. The relevant features were then extracted. A variety of feature selection methods have been utilized resulting in a good performance for the diagnosis of Parkinson. These features were fed to different classifiers so as to be let them decide whether the subjects have the disease or not. Three different classifiers were used in order to bring about a valid classification performance on the given data. The classification performances were compared with one another and showed that the best performance obtained using the KNN classifier with a correct rate of 0.9382. This result reveals that the use of proposed feature selection method results in a desirable precision for the diagnosis of Parkinson’s disease (PD). The performances were assessed from different points of view, providing different aspects of the diagnosis, from which the physicians are able to choose one with higher accuracy in the diagnosis.