Optimization of Features for Classification of Parkinson's Disease from Vocal Dysphonia

Parkinsons disease is considered most prominent neurological disease after Alzheimer and Epilepsy. There is no defined test for early diagnosis of Parkinson's patient and medical decisions are provided based on the medical history of the patient and hence the possibility of misdiagnosis. Parkinsons disease influxes different prospects of a patient and in 90% cases vocal dysphonia is present an analysis of the vocal dysphonia can be considered as the early biomarker of decision making for medical practitioners and neurologists as well as biometric analysis. This study aims at vocal dysphonia analysis of Parkinson's patient from voice dataset with different machine learning algorithms with a goal to achieve better performance with less number of attributes. A comparative study is performed where k-Nearest performed approximately with 98% accuracy with 5 relevant attributes, Random Tree with 100% accuracy with 1 related attribute. In addition in the case of Multi-Layer Perceptions with different hiddenlayers, the performance is evaluated. It is observed that MDVP: Fo, MDVP: Shimmer, RPDE, Spread1 attributes contribute more to efficient classification accuracy.