RESEARCH ARTICLE
Towards Early Intervention: Detecting Parkinson's Disease through Voice Analysis with Machine Learning
K.P. Swain1, S. R. Samal2, Vinayakumar Ravi6, *, Soumya Ranjan Nayak3, Tahani Jaser Alahmadi7, *, Prabhishek Singh4, Manoj Diwakar5
Article Information
Identifiers and Pagination:
Year: 2024Volume: 18
E-location ID: e18741207294056
Publisher ID: e18741207294056
DOI: 10.2174/0118741207294056240322075602
Article History:
Received Date: 02/01/2024Revision Received Date: 24/02/2024
Acceptance Date: 11/03/2024
Electronic publication date: 30/04/2024
Collection year: 2024
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Introduction/ Background
This study aims to utilize machine learning algorithms for early detection of Parkinson's Disease (PD) through voice recording analysis. Employing advanced machine learning techniques and a comprehensive dataset of voice samples, the objective is to develop a non-invasive, accurate, and reliable method for PD diagnosis, contributing to early intervention and management of the disease.
Parkinson's Disease (PD) is a prevalent neurodegenerative disorder impacting millions globally. Early and accurate diagnosis is crucial for effective management and treatment. This study leverages Machine Learning (ML) algorithms to analyze voice recordings, aiming to improve PD detection.
Materials and Methods
We utilized a dataset of 195 voice samples with 23 attributes, applying machine learning algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). The dataset was preprocessed, balanced, and evaluated using various performance metrics.
Results
The K-Nearest Neighbors (KNN) model demonstrated superior performance, achieving high precision (0.96-1.00), recall (0.97-1.00), and F1-scores (0.98-0.99) for both PD and non-PD classes, demonstrating an overall accuracy of 0.98 across 59 samples. This showcases its effectiveness in PD detection via voice analysis.
Discussion
This research underscores the potential of ML in revolutionizing PD detection through non-invasive methods. By comparing various algorithms, the study not only identifies the most effective model but also contributes to the broader understanding of applying ML techniques in healthcare.
Conclusion
The study's findings advocate for the KNN model as a promising tool for early and accurate PD diagnosis through voice analysis. The success of this model opens avenues for future research, including the exploration of more advanced algorithms and the integration of these models into practical diagnostic applications.
