Name
Abstract | ||
|---|---|---|
In this paper, different types of classification methods are compared for effective diagnosis of Parkinson's diseases. The reliable diagnosis of Parkinson's disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approaches described in this paper purpose to efficiently distinguish healthy individuals. Four independent classification schemas were applied and a comparative study was carried out. These are Neural Networks, DMneural, Regression and Decision Tree respectively. Various evaluation methods were employed for calculating the performance score of the classifiers. According to the application scores, neural networks classifier yields the best results. The overall classification score for neural network is 92.9%. Moreover, we compared our results with the result that was obtained by kernel support vector machines [Singh, N., Pillay, V., & Choonara, Y. E. (2007). Advances in the treatment of Parkinson's disease. Progress in Neurobiology, 81, 29-44]. To the best of our knowledge, our correct classification score is the highest so far. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.eswa.2009.06.040 | Expert Syst. Appl. |
Keywords | Field | DocType |
paper purpose,correct classification score,sas base software,parkinson disease,neural network,application score,neural networks,classification method,best result,multiple classification method,performance score,decision tree,overall classification score,independent classification schema,regression,classification methods,effective diagnosis,comparative study,support vector machine | Decision tree,Data mining,Computer science,Artificial intelligence,Classifier (linguistics),Artificial neural network,Schema (psychology),Kernel (linear algebra),Disease,Regression,Pattern recognition,Support vector machine,Machine learning | Journal |
Volume | Issue | ISSN |
37 | 2 | Expert Systems With Applications |
Citations | PageRank | References |
75 | 2.95 | 8 |
Authors | ||
1 | ||