Skip to main content
Article
Analysis of Parkinson's Disease Data
Procedia Computer Science
  • Ram Deepak Gottapu
  • Cihan H. Dagli, Missouri University of Science and Technology
Abstract

In this paper, we investigate the diagnostic data from patients suffering with Parkinson's disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson's research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson's Disease diagnosis to certain extent. We use Parkinson's Progression Markers Initiative (PPMI) dataset provided by Michael J. Fox Foundation to perform our analysis.

Meeting Name
Complex Adaptive Systems Conference with Theme: Cyber Physical Systems and Deep Learning, CAS 2018 (2018: Nov. 5-7, Chicago, IL)
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
  • Convolutional neural network (CNN),
  • Long Short Term Memory (LSTM),
  • Parkinson's,
  • Unified Parkinson Disease Rating Scale (UPDRS)
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2019 The Authors, All rights reserved.
Creative Commons Licensing
Creative Commons Attribution-Noncommercial-No Derivative Works 4.0
Publication Date
11-1-2018
Citation Information
Ram Deepak Gottapu and Cihan H. Dagli. "Analysis of Parkinson's Disease Data" Procedia Computer Science Vol. 140 (2018) p. 334 - 341 ISSN: 1877-0509
Available at: http://works.bepress.com/cihan-dagli/169/