Conventional Support Vector Machines vs. Quantum Support Vector Machines in Parkinson’s Comparative studies in the analysis of disease data

DOI

10.2991/978-94-6463-823-3_56How to use a DOI?

Keywords

Parkinson’s Busking; Support vector machine; Quantum Support Vector Machine

Abstract

As a degenerative disease that seriously affects the central nervous system, early diagnosis of Parkinson’s disease is important for slowing down the course of the disease and improving patient’s quality of life. In recent years, machine learning has shown great potential in medical data analysis, and support vector machines (SVMs) are widely used in classification and prediction tasks. However, traditional SVMs have some limitations in high dimensionality and complex data processing. With the rapid development of quantum computing, quantum support vector machines (QSVMs) have become an emerging technology that has attracted much attention due to its ability to process large-scale data and its potential for modeling high-dimensional features. In this paper, we compare traditional SVMs and QSVMs in the analysis of Parkinson’s disease data, and evaluate their performance in terms of classification accuracy. The experiments are conducted on a publicly available dataset and analyzed in detail through the steps of model construction, data preprocessing, algorithm implementation, and performance evaluation. The results show that QSVM outperforms traditional SVM in complex feature space, which demonstrates its potential advantages and application prospects in medical data analysis.

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.

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TY  - CONF
AU  - Zixiu Li
PY  - 2025
DA  - 2025/08/31
TI  - Conventional Support Vector Machines vs. Quantum Support Vector Machines in Parkinson’s Comparative studies in the analysis of disease data
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 556
EP  - 569
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_56
DO  - 10.2991/978-94-6463-823-3_56
ID  - Li2025
ER  -