Department of Computer, Islamic Azad University, Gorgan Branch, Gorgan, Iran.
J Healthc Eng. 2022 Jun 23;2022:5524852. doi: 10.1155/2022/5524852. eCollection 2022.
Parkinson's disease (PD) is a neurodegenerative nervous system disorder that mainly affects body movement, and it is one of the most common diseases, particularly in elderly individuals. This paper proposes a new machine learning approach to predict Parkinson's disease severity using UCI's Parkinson's telemonitoring voice dataset. The proposed method analyses the patient's voice data and classifies them into "severe" and "nonsevere" classes. At first, a subset of features was selected, then a novel approach with a combination of Rotation Forest and Random Forest was applied on selected features to determine each patient's disease severity. Analysis of the experimental results shows that the proposed approach can detect the severity of PD patients in the early stages. Moreover, the proposed model is compared with several algorithms, and the results indicate that the model is highly successful in classifying records and outperformed the other methods concerning classification accuracy and 1-measure rate.
帕金森病（PD）是一种主要影响身体运动的神经退行性疾病系统障碍，是最常见的疾病之一，尤其是在老年人中。本文提出了一种新的机器学习方法，使用 UCI 的帕金森远程监测语音数据集来预测帕金森病的严重程度。该方法分析患者的语音数据，并将其分为“严重”和“非严重”两类。首先，选择了一组特征，然后应用了一种旋转森林和随机森林相结合的新方法来对选定的特征进行分析，以确定每个患者的疾病严重程度。实验结果的分析表明，该方法可以在早期检测 PD 患者的严重程度。此外，还将提出的模型与几种算法进行了比较，结果表明，该模型在分类记录方面非常成功，在分类准确率和 1 度量率方面均优于其他方法。
