Name
Title | ||
|---|---|---|
A comparison of regression methods for remote tracking of Parkinson's disease progression |
Abstract | ||
|---|---|---|
Remote patient tracking has recently gained increased attention, due to its lower cost and non-invasive nature. In this paper, the performance of Support Vector Machines (SVM), Least Square Support Vector Machines (LS-SVM), Multilayer Perceptron Neural Network (MLPNN), and General Regression Neural Network (GRNN) regression methods is studied in application to remote tracking of Parkinson's disease progression. Results indicate that the LS-SVM provides the best performance among the other three, and its performance is superior to that of the latest proposed regression method published in the literature. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2011.11.067 | Expert Syst. Appl. |
Keywords | Field | DocType |
support vector machines,latest proposed regression method,remote patient tracking,multilayer perceptron neural network,square support,general regression neural network,regression method,disease progression,vector machines,remote tracking,best performance,regression | Least squares,Data mining,General regression neural network,Parkinson's disease,Unified Parkinson's disease rating scale,Regression,Computer science,Support vector machine,Disease progression,Artificial intelligence,Machine learning,Patient Tracking | Journal |
Volume | Issue | ISSN |
39 | 5 | 0957-4174 |
Citations | PageRank | References |
12 | 0.66 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ömer Eskidere | 1 | 31 | 2.48 |
| Figen Ertaş | 2 | 22 | 1.57 |
| Cemal Hanilçi | 3 | 171 | 11.23 |