Prediction of Parkinson's Disease Using Deep Learning in TensorFlow

One of the most common neurodegenerative disorders of the present age is Parkinson’s Disease or Parkinsonism. To estimate its advancement in the patient, huge amounts of data are being collected and studied to draw out inferences. The types of data generally studied towards that end are vocal data, body movement data, eye movement data, handwriting and drawing patterns, etc. In this work, the use of a Deep Neural Network has been proposed which can predict the Unified Parkinson's Disease Rating Scale (UPDRS) both motor and total by studying vocal data from UCI Machine Learning Repository. Both 2 layered as well as 3 layered networks were studied and it was found that the performance of 3-layer Deep Neural Network having 10, 20, 10 neurons in different layers was found to be the best with an accuracy of 97% and 99.62% for motor UPDRS and total UPDRS respectively. The other three parameters MSE, MAE and RMSE also showed improvement in the 3 layered model as compared to the 2 layered model.