| Issue |
E3S Web Conf.
Volume 309, 2021
3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
|
|
|---|---|---|
| Article Number | 01008 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/e3sconf/202130901008 | |
| Published online | 07 October 2021 | |
A Study On Deep Learning And Machine Learning Techniques On Detection Of Parkinson’s Disease
1 MTech Student, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
2 Professor, Computer Science and Engineering, GRIET, Hyderabad, Telangana, India.
* Corresponding author: monika.pilla@gmail.com
Parkinson’s disease (PD) is a sophisticated anxiety malady that impairs movement. Symptoms emerge gradually, initiating with a slight tremor in only one hand occasionally. Tremors are prevalent, although the condition is sometimes associated with stiffness or slowed mobility. In the early degrees of PD, your face can also additionally display very little expression. Your fingers won’t swing while you walk. Your speech can also additionally grow to be gentle or slurred. PD signs and symptoms get worse as your circumstance progresses over time. The goal of this study is to test the efficiency of deep learning and machine learning approaches in order to identify the most accurate strategy for sensing Parkinson’s disease at an early stage. In order to measure the average performance most accurately, we compared deep learning and machine learning methods.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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