Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 35, 26 April 2024
Open
Access | Article
Comparison and analysis of multiple machine learning algorithms on prediction accuracy in Parkinson's patients
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Abstract
This paper describes an experiment on Parkinson’s disease classification using multiple classification algorithms for comparison. Parkinson’s disease is a common neurological disorder, and early diagnosis and classification are important for the assessment of treatment and prognosis. Therefore, the research implications of this paper are clear. The classification algorithms used in the experiment include adaboost classification model, XGBoost classification model, logistic regression regression model, random forest plain Bayesian classification model, bp neural network and support vector machine. The experimental results show that adaboost classification model performs well when dealing with small sample data, XGBoost classification model performs well when dealing with large-scale datasets, and logistic regression regression model and random forest plain Bayesian classification model also have good performance. The bp neural network and support vector machine, on the other hand, perform poorly in terms of classification results and require a much larger dataset for support. These experimental results have important reference value for the classification and diagnosis of Parkinson’s disease. Different classification algorithms are suitable for different dataset sizes and characteristics, so in practical applications, we can choose different classification algorithms according to the size and characteristics of the dataset to achieve the optimal classification effect. In conclusion, the results of this paper provide a reference for the classification and diagnosis of Parkinson’s disease, as well as a guide for choosing appropriate classification algorithms. In the future, we can further expand the dataset size and use more classification algorithms for comparison to improve the accuracy and robustness of Parkinson’s disease classification.
Keywords
Parkinson’s patients, Machine learning, Prediction accuracy
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Modern Medicine and Global Health
- ISBN (Print)
- 978-1-83558-395-1
- ISBN (Online)
- 978-1-83558-396-8
- Published Date
- 26 April 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/35/20240874
- Copyright
- 26 April 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited