PD-ADSV: An Automated Diagnosing System Using Voice Signals and Hard Voting Ensemble Method for Parkinson's Disease
This addresses the need for more accessible and accurate diagnosis tools for Parkinson's disease patients, but appears incremental as it combines existing methods.
The study tackled Parkinson's disease diagnosis by developing an automated system using voice signals and a hard voting ensemble method, achieving the highest accuracy, though no specific numbers were provided.
Parkinson's disease (PD) is the most widespread movement condition and the second most common neurodegenerative disorder, following Alzheimer's. Movement symptoms and imaging techniques are the most popular ways to diagnose this disease. However, they are not accurate and fast and may only be accessible to a few people. This study provides an autonomous system, i.e., PD-ADSV, for diagnosing PD based on voice signals, which uses four machine learning classifiers and the hard voting ensemble method to achieve the highest accuracy. PD-ADSV is developed using Python and the Gradio web framework.