One of the most catastrophic neurological disorders worldwide is Parkinson's Disease. Along with it, the treatment is complicated and abundantly expensive. The only effective action to control the progression is diagnosing it in the early stage. However, this is challenging because early detection necessitates a large and complex clinical study. This experimental work used Machine Learning techniques to automate the early detection of Parkinson's Disease from clinical characteristics, voice features and motor examination. In this study, we develop ML models utilizing a public dataset of 130 individuals, 30 of whom are untreated Parkinson's Disease patients, 50 of whom are Rapid Eye Movement Sleep Behaviour Disorder patients who are at a greater risk of contracting Parkinson's Disease, and 50 of whom are Healthy Controls. We use MinMax Scaler to rescale the data points, Local Outlier Factor to remove outliers, and SMOTE to balance existing class frequency. Afterwards, apply a number of Machine Learning techniques. We implement the approaches in such a way that data leaking and overfitting are not possible. Finally, obtained 100% accuracy in classifying PD and RBD patients, as well as 92% accuracy in classifying PD and HC individuals.

翻译：帕金森病是全球最具破坏性的神经系统疾病之一，其治疗过程复杂且费用高昂。控制病情进展的唯一有效手段是早期诊断，然而这一目标因早期检测需要开展大规模、复杂的临床研究而极具挑战性。本实验研究采用机器学习技术，通过临床特征、声音特征和运动检查实现帕金森病的自动化早期检测。研究中，我们利用包含130名个体的公开数据集（其中30名为未接受治疗的帕金森病患者，50名为具有较高帕金森病发病风险的快速眼动睡眠行为障碍患者，50名为健康对照组），开发了机器学习模型。采用MinMax Scaler对数据点进行重缩放，通过Local Outlier Factor剔除异常值，并运用SMOTE平衡各类别样本频率。随后应用多种机器学习技术，确保实施过程中避免数据泄露和过拟合问题。最终，模型对帕金森病与快速眼动睡眠行为障碍患者的分类准确率达100%，对帕金森病与健康对照组的分类准确率达92%。