Name
Title | ||
|---|---|---|
Remote tracking of Parkinson's Disease progression using ensembles of Deep Belief Network and Self-Organizing Map |
Abstract | ||
|---|---|---|
•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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.eswa.2020.113562 | Expert Systems with Applications |
Keywords | DocType | Volume |
Deep Learning,Parkinson's Disease,Clustering,Unified Parkinson's Disease Rating Scale,Predictive Accuracy | Journal | 159 |
ISSN | Citations | PageRank |
0957-4174 | 1 | 0.37 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mehrbakhsh Nilashi | 1 | 313 | 24.80 |
| Hossein Ahmadi | 2 | 467 | 28.84 |
| Abbas Sheikhtaheri | 3 | 19 | 8.41 |
| Roya Naemi | 4 | 1 | 0.37 |
| Reem Alotaibi | 5 | 5 | 1.77 |
| Ala Abdulsalam AlArood | 6 | 3 | 1.07 |
| Asmaa Munshi | 7 | 2 | 1.09 |
| Tarik Rashid | 8 | 19 | 9.27 |
| Jing Zhao | 9 | 1 | 0.37 |