Year: 2025
Type: article
Abstract: Parkinson's Disease (PD) is traditionally diagnosed through clinical assessments, primarily focusing on motor symptoms, which can result in delayed detection. These systems often fail to account for non-motor symptoms, such as subtle vocal changes, that may precede more apparent signs of the disease. To address this gap, we propose a machine learning approach utilizing voice analysis for the early prediction of PD. This study applies Support Vector Machines (SVMs) to analyze vocal features like jitter, shimmer, and Harmonics-to-Noise Ratio (HNR) from a dataset of 195 voice samples collected from 31 participants, 23 of whom are diagnosed with PD. Using SVM with a linear kernel, we achieved an accuracy of 87% and a precision of 91% in classifying individuals with PD based on vocal characteristics. This non-invasive technique highlights the potential of vocal feature analysis as an early diagnostic tool for PD, allowing for earlier interventions. Our study demonstrates the effectiveness of SVM in high-dimensional data spaces and its robustness against overfitting, offering a promising enhancement to current diagnostic methods for Parkinson's Disease.
Language: English
Topic: Voice and Speech Disorders
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4410096405