Intelligent Parkinson’s Disease Detection: Optimization Algorithm Implementation for SVM and MLP Classifiers on Voice Bio-Markers

DOI

10.2991/978-94-6463-471-6_23How to use a DOI?

Keywords

Support vector machine; multilayer perceptron; principal component analysis; machine learning; voice analysis

Abstract

Parkinson's disease is a disorder of the nervous system that causes impairment and changes in cognitive behavior. Voice analysis has become a crucial tool for diagnosing neurological conditions like PD, with symptoms typically appearing in people aged 50 or older. This research suggests new methods to improve early PD diagnostic methods, focusing on assessing aspects and fine-tuning hyperparameters of machine learning algorithms. The data set includes characteristics of both healthy and PD patients, aged 50 to 85. After processing, pertinent characters or traits are extracted from those voice recordings. In this research paper, we investigate Principal Component Analysis (PCA) for feature selection in conjunction with optimization techniques for training Support Vector Machine and Multilayer Perceptron models. The optimization techniques employed include the Firefly Algorithm, Particle Swarm Optimization (PSO), Grasshopper Optimizer, Grey Wolf Optimizer, and Genetic Algorithm (GA). Our study aims to assess the effectiveness of these optimization algorithms in enhancing the performance of MLP and SVM models on the dataset of Parkinson. The MLP and SVM accuracy rates of the optimization algorithms Firefly, PSO, Genetic, Grey Wolf, and Grasshopper were high; Firefly reached 97% (MLP) and 92% (SVM) accuracy, PSO 82% and 94.87% accuracy, while Genetic, Grasshopper, and Greywolf obtained 82% and 94% accuracy, respectively.

Copyright

© 2024 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  - Panduranga Vital Terlapu
AU  - Malla Swetha
AU  - Jami Sai Ram
AU  - Korlam Sai Srinivas
AU  - Bellala Sai Nataraj
AU  - Malla Lahari
AU  - Godugoti Sowjanya
AU  - Bellala Sai Deexitha
AU  - Maddula Ratna Mohitha
PY  - 2024
DA  - 2024/07/30
TI  - Intelligent Parkinson’s Disease Detection: Optimization Algorithm Implementation for SVM and MLP Classifiers on Voice Bio-Markers
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 230
EP  - 241
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_23
DO  - 10.2991/978-94-6463-471-6_23
ID  - Terlapu2024
ER  -