Abstract
Generally, Parkinson’s disease (PD) in medicine is a long-term neurodegenerative and progressive disorder. In some brain parts, as the dopamine generating neurons die or they are damaged. Then people begin to have difficulty in walking, writing, speaking or making other basic missions Some of the indications of the disease worsen over time and thus result in increased acuteness of Parkinson's disease. We have proposed a methodology for the prognosis of Parkinson’s disease acuteness. In this scientific article, we used deep neural networks in UCI's Parkinson's telemonitoring voice dataset patients. We have utilized Keras and TensorFlow in Python deep learning library to implement our neural network for prognosis the PD acuteness. The correctness values obtained with our method are preferable than the correctness values specified in the previous research test. Key words: Parkinson's disease, Deep Learning, UCI, Python, Deep Neural Network, Keras, TensorFlow, UPDRS
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