Parkinson's disease (PD) is a prevalent neurodegenerative disorder known for its impact on motor neurons, causing symptoms like tremors, stiffness, and gait difficulties. This study explores the potential of vocal feature alterations in PD patients as a means of early disease prediction. This research aims to predict the onset of Parkinson's disease. Utilizing a variety of advanced machine-learning algorithms, including XGBoost, LightGBM, Bagging, AdaBoost, and Support Vector Machine, among others, the study evaluates the predictive performance of these models using metrics such as accuracy, area under the curve (AUC), sensitivity, and specificity. The findings of this comprehensive analysis highlight LightGBM as the most effective model, achieving an impressive accuracy rate of 96% alongside a matching AUC of 96%. LightGBM exhibited a remarkable sensitivity of 100% and specificity of 94.43%, surpassing other machine learning algorithms in accuracy and AUC scores. Given the complexities of Parkinson's disease and its challenges in early diagnosis, this study underscores the significance of leveraging vocal biomarkers coupled with advanced machine-learning techniques for precise and timely PD detection.

翻译：帕金森病是一种常见的神经退行性疾病，以影响运动神经元而闻名，会导致震颤、僵硬和步态困难等症状。本研究探索帕金森病患者声学特征变化作为疾病早期预测手段的潜力，旨在预测帕金森病的发病。研究采用包括XGBoost、LightGBM、Bagging、AdaBoost和支持向量机等多种先进机器学习算法，并通过准确率、曲线下面积、灵敏度和特异度等指标评估这些模型的预测性能。综合分析结果表明，LightGBM是最有效的模型，实现了96%的显著准确率和相匹配的96% AUC值。LightGBM显示出100%的卓越灵敏度和94.43%的特异度，在准确率和AUC分数上均优于其他机器学习算法。鉴于帕金森病的复杂性及其早期诊断的挑战性，本研究强调了结合声学生物标志物与先进机器学习技术对于实现帕金森病精准及时检测的重要意义。