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.
