Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map
作者：
Highlights：
• A new method is developed for remote tracking of Parkinson's Disease progression.
• The method is developed thorough cluster analysis and deep learning.
• The deep learning is performed using Deep Belief Network.
• Cluster analysis is performed using Self-Organizing Map.
• The accuracy improvement in UPDRS prediction was significant.
摘要
•A new method is developed for remote tracking of Parkinson's Disease progression.•The method is developed thorough cluster analysis and deep learning.•The deep learning is performed using Deep Belief Network.•Cluster analysis is performed using Self-Organizing Map.•The accuracy improvement in UPDRS prediction was significant.
论文关键词：Deep Learning,Parkinson's Disease,Clustering,Unified Parkinson's Disease Rating Scale,Predictive Accuracy
论文评审过程：Received 10 August 2019, Revised 11 May 2020, Accepted 11 May 2020, Available online 15 May 2020, Version of Record 5 June 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113562
