期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 159, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113562
关键词
Deep Learning; Parkinson's Disease; Clustering; Unified Parkinson's Disease Rating Scale; Predictive Accuracy
Parkinson's Disease (PD) is one of the most prevalent neurological disorders characterized by impairment of motor function. Early diagnosis of PD is important for initial treatment. This paper presents a newly developed method for application in remote tracking of PD progression. The method is based on deep learning and clustering approaches. Specifically, we use the Deep Belief Network (DBN) and Support Vector Regression (SVR) to predict Unified Parkinson's Disease Rating Scale (UPDRS). The DBN prediction models were developed by different epoch numbers. We use a clustering approach, namely, Self Organizing Map (SOM), to improve the accuracy and scalability of prediction. We evaluate our method on a real-world PD dataset. In all, nine clusters were detected from the data with the best SOM map quality for clustering, and for each cluster, a DBN was developed with a specific number of epochs. The results of the DBN prediction models were integrated by the SVR technique. Further, we compare our work with other supervised learning techniques, SVR and Neuro-Fuzzy techniques. The results revealed that the hybrid of clustering and DBN with the aid of SVR for an ensemble of the DBN outputs can make relatively better predictions of Total-UPDRS and Motor-UPDRS than other learning techniques. (c) 2020 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据