A Feature Driven Intelligent System for Neurodegenerative Disorder Detection: An Application on Speech Dataset for Diagnosis of Parkinson's Disease

Abstract

Parkinson's disease (PD) is a prevalent, and progressive neurological disorder. Due to the motor and non-motor symptoms of the disease, it lowers life quality of the patients. Tremor, rigidity, depression, anxiety etc. are among the symptoms. Clinical diagnosis of PD is usually based on appearance of motor features. Additionally, different empirical tests were proposed by scholars for early detection of the disease. It is known that people with PD have speech impairments. Therefore, voice tests are used for early detection of the disease. In this study, an automated machine learning system was proposed for high accuracy classification of the speech signals of PD patients. The system includes feature reduction methods and classification algorithms. Feature reductions and classifications were performed for all participants, males, and females separately. Contributions of feature sets to classification accuracy were discussed. Experimental results were evaluated with different performance metrics. The proposed system obtained state of the art results in all categories. We acquired better performances for gender based classifications.