A Proficient System for Detecting Parkinson’s Disease

Abstract

Data mining techniques have been employed in a wide range of applications. One of the applications is in the analysis of medical data. Parkinson’s disease (PD) is a nervous system based condition which results in the damage of brain cells. The symptoms of the disease increase with time. The symptoms include difficulty in movement, decrease in thinking ability and finally leads to dementia. Parkinson’s disease is generally observed in individuals above the age of sixty. Currently, the detection of Parkinson’s disease is done using MRI, PET scans and MMSE (Mini Metal State Exams). But, the brain patterns and image intensities of individuals with Alzheimer’s share similarity with individuals with Parkinson’s disease s(PD). There is a need for a more accurate means of early detection of the disease, our proposed system is the combination of predictive analysis framework which uses Parkinson’s voice dataset and spiral drawing inputs of normal and Parkinson’s affected patients