Advanced And Effective Classification of Parkinson’s Disease Using Enhanced Neural Networks

A disease that causes due to degeneration in the nerve cells in brain parts with which movements are controlled is a Parkinson's disease. Parkinson’s disease increased its occurrence with age and it's becoming the second most common neurodegenerative disorder. Due to this disease, people begin to experience difficulty in speaking, writing, walking. We can diagnose this disease by using voice recordings dataset, handwritten images, and by using several techniques. In this paper, we are using a voice dataset to identify Parkinson's disease which is collected from different people with and without Parkinson's disease. There are several medical tools to identify this disease but it may take time and laborious to identify this disease. In this patients has to take any number of tests as a single test cannot give an effective diagnosis of a disease. So to mitigate this, our work establishes a computer-based analysis technique. We can identify and diagnose this disease using several classification algorithms in machine learning like decision trees, naive bayes algorithm, SVM algorithm are few neural networks. In this paper, we use neural networks to classify the disease.