Analysing the Performance of Support Vector Machine Algorithm in Predicting Parkinson’s Disease

Abstract

Parkinson’s disease (PD) is a neurodegenerative measure disease where the symptoms steadily develop start with an insignificant a slight shaking movement in one hand and a feeling of stiffness in the body and it became worse over time. Dopamine is most notably involved in helping us feel preference as part of the brain's reward system. It acts as a message between the parts of the brain and nervous system that support control and co-ordinate body movements. As dopamine generally neurons in the parts begin to experience difficulty in speaking, writing, walking or completing another simple task. Virtually, 90% pretentious people with Parkinson have tongue disorders. It affects over 6 million people worldwide. At present there is no conclusive result for this disease by non-specialist clinicians, particularly in the early stage of the disease where identification of the symptoms is very difficult in its earlier stages. The future predictive analytics outline is a combination of K-means clustering and Decision Tree which is used to improvement visions from patients. By using machine learning methods, the problem can be explained with negligible error rate. The investigated approach serves as a guide in determining the diagnosis and in planning the appropriate by a loss of nerve cells in the part of the brain