Detection of Parkinson’s Disease using XGBoost and Convolutional Neural Networks
Authors
R. Vijayalakshmi1, *, O. R. G. Ravikumar2, T. G. Vikram Ganesh2, A. S. Arunkumar2
1Associate Professor, Department of Computer Science and Engineering, Velammal College of Engineering and Technology Madurai, Madurai, Tamil Nadu, India
2UG Student, Department of Computer Science and Engineering, Velammal College of Engineering and Technology Madurai, Madurai, Tamil Nadu, India
*Corresponding author.
Email: rvl@vcet.ac.in
Corresponding Author
R. Vijayalakshmi
Available Online 23 May 2025.
- DOI
- 10.2991/978-94-6463-718-2_110How to use a DOI?
- Keywords
- Parkinson’s Disease; XGBoost; Convolutional Neural Network (CNN); Early Detection; Machine Learning Diagnostics
- Abstract
It is important to be able to predict parkinsons disease because it can improve a patient’s health. The study explores machine learning prediction methods such as XGBoost and Convolutional Neural Networks for Parkinson’s disease prediction. Traditional Child Models These methods are effective in exploring medical data and searching for a pattern These new models are then more accurate and reliable in comparison to earlier methods and can aid in timely disease detection.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - R. Vijayalakshmi AU - O. R. G. Ravikumar AU - T. G. Vikram Ganesh AU - A. S. Arunkumar PY - 2025 DA - 2025/05/23 TI - Detection of Parkinson’s Disease using XGBoost and Convolutional Neural Networks BT - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024) PB - Atlantis Press SP - 1320 EP - 1331 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-718-2_110 DO - 10.2991/978-94-6463-718-2_110 ID - Vijayalakshmi2025 ER -