Identification of dysphonia related to parkinson’s disease using parametric and non parametric models

Abstract

Classification of Parametric and Non Parametric models is done by using the collected dataset of Parkinson's disease.
Testing is done on Parkinson’s data set with two respective models to determine which model provides the higher
classification accuracy. Logistic Regression technique is used to classify the Parkinson's data using non parametric
modeling and K-Nearest Neighbors and Random Forest Algorithm is used to classify the training and test data of
Parkinson’s disease for parametric model. Based on the data classification, , we obtain the result using parametric and non
parametric models. Finally, Comparison is made on of both Parametric and Non Parametric model to evaluate the
performance of the Parkinson's dataset.