Early Detection of Parkinson Disease Throughbiomedical Speech and Voice Analysis

Parkinson's disease, widely recognized as a neurodegenerative condition characterized by subtle changes in voice, has spurred an investigation into voice analysis for diagnostic purposes. This study is dedicated to the early detection of Parkinson's disease through a comprehensive examination of biomedical speech attributes. Parameters such as fundamental frequency range, jitter, shimmer, noise-to-harmonics ratio, and features derived from nonlinear analysis are considered, alongside variables like status, indicating the presence of neurological disorders, and class for classification purposes. Together, these attributes provide a detailed representation of voice signals, offering valuable insights into both neurological and voice disorders for research purposes. The dataset exhibits promising potential for applications in medical diagnostics and voice analysis. In the pursuit of accurate disease detection, various machine learning methodologies are employed, including Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), Neural Networks (NN), and state- of-the-art Convolutional Neural Networks (CNNs). The incorporation of CNNs is pivotal, signifying a significantleap in accuracy of 100%for disease detection. The results showcase a model adept at discerning subtle changesassociated with Parkinson's disease, with SVM achieving 96%, Decision Tree demonstrating a perfect 100%, Neural Network attaining 98%, and Random Forest showcasing an accuracy of 99%. This innovative approach not only transforms early Parkinson's disease identification through voice analysis, setting a precision benchmark, but also underscores the transformative potential of cutting-edge technologies in healthcare practices. The study positions the model as a reliable diagnostic tool, capable of advancing medical diagnosticsthrough the seamless integration of biomedical research and machine learning, contributing to the broader fieldof neurodegenerative disease diagnostics.