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.
Description:
Parkinson’s disease is a progressive neuro-degenerative disease which affects in our central
nervous system. Our brain consists 86 billion neurons. Every neuron has his own functionality. In
our mid brain have an area called substantia nigra . In this have a neuron called dopamine.
Reducing dopamine neurons is the causes of being Parkinson’s disease in human body.
Approximately 4-6 million people are affected by Parkinson’s disease in every year.
In Bangladesh, Every year 1600 people are die by Parkinson’s disease and every year the growth
rate of this disease are getting larger. The life expectancy of PD affected people is 5-10 years. But
the pooled report on clinical research says that [16] accuracy of diagnosis Parkinson’s disease is
only 80.6%. Till today has no recovery of [3] Parkinson’s disease(PD).In the early stage diagnosis
parkinson’s disease by using some brain scans like MRI, fMRI, SPECT etc. But it can’t give the
best accuracy.
So for that we are come up to work with it by different approaches to improving the diagnosis of
PD research that [3] in the early stage can diagnosis Parkinson’s disease(PD) by [20] Support
vector Machine (SVM) , K-nearest neighbor (KNN) and Decision tree (DT). So that for medical
research it can very helpful that which approaches is best for diagnosis PD by using classification
technique.