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A parallel neural network approach to prediction of Parkinson´s Disease
Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology.
Engineering Faculty Esentepe Kampus, Computer Engineering Department, Sakarya University, Turkey.
2011 (English)In: Expert systems with applications, ISSN 0957-4174, Vol. 38, no 10, 12470-12474 p.Article in journal (Refereed) Published
Abstract [en]

Recently the neural network based diagnosis of medical diseases has taken a great deal of attention. In this paper a parallel feed-forward neural network structure is used in the prediction of Parkinson’s Disease. The main idea of this paper is using more than a unique neural network to reduce the possibility of decision with error. The output of each neural network is evaluated by using a rule-based system for the final decision. Another important point in this paper is that during the training process, unlearned data of each neural network is collected and used in the training set of the next neural network. The designed parallel network system significantly increased the robustness of the prediction. A set of nine parallel neural networks yielded an improvement of 8.4% on the prediction of Parkinson’s Disease compared to a single unique network. Furthermore, it is demonstrated that the designed system, to some extent, deals with the problems of imbalanced data sets.

Place, publisher, year, edition, pages
2011. Vol. 38, no 10, 12470-12474 p.
Keyword [en]
Parallel neural networks, Parkinson´s Disease, Decision support system
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-69156DOI: 10.1016/j.eswa.2011.04.028OAI: diva2:424219
Available from: 2011-06-17 Created: 2011-06-17 Last updated: 2013-04-26

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