Abstract: Parkinson’s disease is a progressive and chronic neurodegenerative disorder.That affects about 1 million people in the united states, with approximately 60,000 new clinical diagnoses made each year. Due to motor symptoms, it affects the normal life of a person. There is a severe need to identify PD in its early stage to avoid it getting worse and to control its symptoms easily. As the dopamine generating neurons in parts of the brain become damaged or die, people begin to experience difficulty in speaking, writing, walking, or completing other simple tasks. Therefore it is difficult for doctors it in initial stage. A new methodology is proposed in this project for the prediction of Parkinson’s disease severity using Long Short-Term Memory (LSTM) architecture for Parkinson’s disease diagnosis. ‘keras and TensorFlow’ library is used for the implementation of our neural network to predict the severity.The accuracy values obtained by our method are better as compared to the
accuracy obtained in previous research work.

Keywords: Parkinson”s disease, Deep Neural Network, LSTM, Keras and Tensor Flow, Chronic Neuro Disorder

PDF | DOI: 10.17148/IJARCCE.2021.10754

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