Parkinson’s Disease Diagnosis Based on XGBoost Algorithm

DOI

10.2991/978-94-6463-300-9_2How to use a DOI?

Keywords

Machine Learning; XGBoost; Parkinson’s Disease; Classification

Abstract

In light of China’s aging population and improved living standards, there is an increasing focus on health issues. Parkinson’s disease is a commonly-seen neurodegenerative disorder that greatly impacts patients’ quality of life, and its prevalence is on the rise. The traditional approach to Parkinson’s disease detection relies on subjective symptom assessment by physicians using a standardized rating scale. This approach is susceptible to high misdiagnosis rates, and it is time and labor-intensive. Currently existing Parkinson’s prediction systems pose problems of complicated operation and suboptimal algorithms, which hinders the advancement of experiments. In light of these challenges, this study seeks to improve and enhance commonly-used algorithms to enable more accurate diagnosis of Parkinson’s disease. To facilitate the automated diagnosis and symptom prediction in patients, this study utilizes a Parkinson’s disease voice prediction model that is based on speech analysis and machine learning algorithms. By collecting patients’ voice data and using 22 different parameters, including average vocal fundamental frequency, maximum vocal fundamental frequency, minimum vocal fundamental frequency, and jitter, the model achieves high accuracy in diagnosing and predicting patient symptoms.

Copyright

© 2023 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.

Download article (PDF)

TY  - CONF
AU  - Shilong Yao
PY  - 2023
DA  - 2023/11/27
TI  - Parkinson’s Disease Diagnosis Based on XGBoost Algorithm
BT  - Proceedings of the 2023 International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2023)
PB  - Atlantis Press
SP  - 7
EP  - 15
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-300-9_2
DO  - 10.2991/978-94-6463-300-9_2
ID  - Yao2023
ER  -