Identification of dysphonia related to parkinson’s disease using parametric and non parametric models / 国际药学研究杂志

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.