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.
