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

Abstract

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.