Title: Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning Show Chinese title

Title: 通过声学分析和机器学习早期识别帕金森病

Abstract: Parkinson's Disease (PD) is a progressive neurodegenerative disorder that significantly impacts both motor and non-motor functions, including speech. Early and accurate recognition of PD through speech analysis can greatly enhance patient outcomes by enabling timely intervention. This paper provides a comprehensive review of methods for PD recognition using speech data, highlighting advances in machine learning and data-driven approaches. We discuss the process of data wrangling, including data collection, cleaning, transformation, and exploratory data analysis, to prepare the dataset for machine learning applications. Various classification algorithms are explored, including logistic regression, SVM, and neural networks, with and without feature selection. Each method is evaluated based on accuracy, precision, and training time. Our findings indicate that specific acoustic features and advanced machine-learning techniques can effectively differentiate between individuals with PD and healthy controls. The study concludes with a comparison of the different models, identifying the most effective approaches for PD recognition, and suggesting potential directions for future research. Show Chinese abstract

Abstract: 帕金森病（PD）是一种进行性神经退行性疾病，对运动和非运动功能都有显著影响，包括言语。通过语音分析早期准确地识别PD可以大大提高患者的预后，从而实现及时干预。本文全面回顾了使用语音数据进行PD识别的方法，重点介绍了机器学习和数据驱动方法的进展。我们讨论了数据处理的过程，包括数据收集、清洗、转换和探索性数据分析，以准备用于机器学习应用的数据集。探讨了各种分类算法，包括逻辑回归、支持向量机和神经网络，有无特征选择。每种方法均根据准确性、精确性和训练时间进行评估。我们的研究结果表明，特定的声学特征和先进的机器学习技术可以有效地区分PD患者和健康对照组。研究最后比较了不同模型，确定了最有效的PD识别方法，并提出了未来研究的潜在方向。