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.

翻译：帕金森病（PD）是发病率最高的运动障碍性疾病，也是仅次于阿尔茨海默病的第二大常见神经退行性疾病。目前，运动症状评估与影像学技术是该疾病最主流的诊断方式，但这些方法在准确性和时效性方面存在局限，且往往仅适用于有限人群。本研究提出一种基于语音信号的自主诊断系统PD-ADSV，该系统融合四种机器学习分类器与硬投票集成方法，实现了最高诊断准确率。PD-ADSV使用Python语言及Gradio网络框架进行开发。