A Study on Parkinson's Disease Parameters Using Data Mining with Machine Learning Approaches

Abstract

Parkinson's disease is a long-term condition that demands continuous attention and care. While it can substantially affect an individual's quality of life, with appropriate treatment and support, people diagnosed with Parkinson's can enjoy meaningful lives for numerous years. Research into Parkinson's disease remains active, with ongoing progress in comprehending the condition and discovering innovative treatment options. Data mining, also known as Knowledge Discovery in Database (KDD), is a highly valuable technique employed by entrepreneurs, researchers, and individuals for extracting valuable insights from extensive data collections. The knowledge discovery process encompasses several key steps, including data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation.This paper considers Parkinson’s disease Data Set.The machine learning approaches which is used to analysis and predict the dataset usinglinear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.