A Knowledge Based Data Mining Based on Parkinson’s Desease

Abstract

The approaches of detecting Parkinson’s disease (PD) in human body from voice data using Classification techniques apply three different algorithms for finding the growth rate of Parkinson’s disease. Support vector machine (SVM), K-nearest neighbor (K-NN) and Decision tree (DT) are applied to detect the progression rate of Parkinson’s disease (PD) by using Unified Parkinson’s disease rating scale (UPDRS) and Hoehn &Yahr scale (HYS) . Unified Parkinson’s disease rating scale (UPDRS) deals with motor fluctuations and change over voice after certain period and that can measure the people affected by Parkinson’s disease and healthy people. Hoehn & Yahr scale (HYS) are measures the symptoms which are related to progression of Parkinson’s disease (PD) in human body. Classifier algorithms used to detect the factors and symptoms which are related to progression of Parkinson’s disease (PD) in human body using voice data. The algorithms can detect Parkinson’s disease (PD) by several approaches which criteria are related to progression of Parkinson’s disease (PD) in human body. From the distinctions of all algorithms measures which algorithm give the best accuracy for several approaches to diagnosis Parkinson’s disease (PD) and risk factors of had Parkinson’s disease (PD) in human body.