Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)

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Parkinson’s Disease Detection by Using Machine Learning

Authors
Y. Sravanthi1, *, P. Soundarya1, S. Rajesh Babu1, Voodara Devender1
1Department of Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India
*Corresponding author. Email: sravanthiyara41@gmail.com
Corresponding Author
Y. Sravanthi
Available Online 31 March 2026.
DOI
10.2991/978-94-6239-628-9_7How to use a DOI?
Keywords
Parkinson’s disease; K-Nearest Neighbours (KNN); Support Vector Machine; Random Forest; AdaBoost; Logistic Regression; Decision Tree; Data pre-processing
Abstract

Parkinson’s Disease (PD) is a long-lasting neurological disorder that often affects speech and language as well as motor function. Speech can be a useful, pain-free biomarker for early diagnosis since the majority of patients (over 90%) exhibit phonetic and speech-related features. as high dimensionality and limited sample size, preprocessing operations like standardization, multicollinearity testing, and dimensionality reduction were applied. Cross validation and hyperparameter tuning were applied in order to train and optimize various machine-learning algorithms: Support Vector Machine (SVM), Random Forest, K-Nearest Neighbours (KNN), AdaBoost, and Logistic Regression, Decision Tree. The best-performing model, Decision Tree, was 94.10 percent accurate, which is about 8 percent better than the current models. These results show the ability of speech and text analysis to be able to diagnose PD non-invasively and efficiently and timely.

Copyright
© 2026 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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Volume Title
Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
Series
Advances in Engineering Research
Publication Date
31 March 2026
ISBN
978-94-6239-628-9
ISSN
2352-5401
DOI
10.2991/978-94-6239-628-9_7How to use a DOI?
Copyright
© 2026 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

risenwbib
TY  - CONF
AU  - Y. Sravanthi
AU  - P. Soundarya
AU  - S. Rajesh Babu
AU  - Voodara Devender
PY  - 2026
DA  - 2026/03/31
TI  - Parkinson’s Disease Detection by Using Machine Learning
BT  - Proceedings of the International Conference on Recent Trends in Intelligent Computing, Manufacturing, and Electronics (rTIME 2025)
PB  - Atlantis Press
SP  - 60
EP  - 70
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-628-9_7
DO  - 10.2991/978-94-6239-628-9_7
ID  - Sravanthi2026
ER  -