Classification of Parkinson's disease based on Multilayer Perceptrons Neural Network

Abstract

Parkinson's disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer's disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD. The dataset information of this project has been taken form the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of 92.95% while SCG obtained 78.21% accuracy.