Abstract：
Parkinson\'s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as \'bradykinesia\', loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.
摘要：
帕金森病(PD)是一种影响神经系统的神经退行性疾病,行为，和大脑的生理系统。这种疾病也被称为震颤。这种疾病的常见症状是运动缓慢，称为“运动迟缓”，自动运动的损失，演讲/写作变化，在早期阶段行走困难。为了解决这些问题并增强PD的诊断过程，机器学习（ML）算法已经实现了主观疾病和健康控制（HC）的分类，具有可比的医学外观。为了提供已用于PD分析和诊断的数据模式和人工智能技术的深远概述，我们对直到2022年发表的研究论文进行了文献分析。本研究共纳入112篇研究论文，检查他们的目标，数据源和不同类型的数据集，ML算法，和相关的结果。结果表明，ML方法和新的生物标志物在临床决策中具有很大的应用前景。导致更系统和知情的PD诊断。在这项研究中,解决了一些主要挑战，并提出了未来的建议。
