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Early
detection of severity of a disease is conducive to treating the patient sooner.
This paper intended to verify the effectiveness of the application of Deep
Learning in the prediction of severity of Parkinson’s disease in a patient
using his or her voice characteristics. The dataset used was UCI’s Parkinson’s
Telemonitoring Dataset, comprising of 16 attributes or biomedical voice measurements
with various range of values from 42 people with early-stage Parkinson’s
disease. It was first pre-processed by applying normalisation. Then the
segmentation of the normalised dataset was done to create training dataset and
testing dataset. Deep Neural Networks were trained on the training data, and
finally the accuracy of severity prediction was obtained by running the network
on the testing data. We were able to successfully implement deep neural network
in predicting the severity of Parkinson’s disease, achieving an accuracy of
81.6 % and 62.7 % in the case of motor-UPDRS and total-UPDRS scores
respectively. In order to analyse the dataset and make an attempt to understand
the trend of these severity scores, an analysis of the normalised dataset was
performed on the basis of gender and age of patients. The results indicate that
accurate prediction of severity of Parkinson’s disease can be done using deep
learning. This implies that Deep Learning can be used for severity prediction
and medical analysis for other diseases of similar types as well. Although we
have used a dataset of 5875 instances, the accuracy of our approach can be
further improved by implementing it on a larger dataset, having more number of
instances of each severity class. Moreover, more number of patient attributes
like- gait and handwriting features- can be added to make the model more
reliable. Also, more powerful computing resources(i.e. GPUs with better
processing capabilities) can be used to improve the time complexity of our approach.

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