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.
Recommended Citation
R. D. Gottapu and C. H. Dagli, "Analysis of Parkinson's Disease Data," Procedia Computer Science, vol. 140, pp. 334 - 341, Elsevier B.V., Nov 2018.
The definitive version is available at https://doi.org/10.1016/j.procs.2018.10.306
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)
International Standard Serial Number (ISSN)
1877-0509
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
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publication Date
01 Nov 2018
