USING DEEP LEARNING ALGORITHM TO DIAGNOSE PARKINSON DISEASE WITH HIGH ACCURACY
Abstract
Early diagnosis of Parkinson's disease, which causes vital and permanent damage to both motor and non-motor symptoms, is very important to prevent further deterioration of the patient condition. In the present study, Parkinson's Disease data set from UCI repository is classified using deep learning architecture. The deep learning architecture in the study is a feed-forward neural network (FFNN) which is builded by Keras of Python. The architecture in the study composes of an input layer, two hidden layers and softmax function with ReLu (Rectified Linear Units) as an output layer. The deep learning architecture solves binary classification problem since PD data set has two classes. In order to classify the PD data set, many tests were performed by splitting the test and train data in different ratios. The PD data set classification was succeeded with 100% accuracy using deep learning algorithm splitting in %20 of the data as the test and the remaining as train data in epoch 30.
Keywords
References
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Details
Primary Language
English
Subjects
Computer Software
Journal Section
Research Article
Publication Date
November 29, 2019
Submission Date
July 22, 2019
Acceptance Date
October 17, 2019
Published in Issue
Year 2019 Volume: 22