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Diagnosing Parkinson’s Diseases using Fuzzy Neural System


Affiliations
1 Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey
2 Department of Electrical and Electronic Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey
 

This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson’s disease. A diagnosing of Parkinson’s diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system.
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  • Diagnosing Parkinson’s Diseases using Fuzzy Neural System

Abstract Views: 90  |  PDF Views: 1

Authors

Rahib H. Abiyev
Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey
Sanan Abizade
Department of Electrical and Electronic Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey

Abstract


This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson’s disease. A diagnosing of Parkinson’s diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system.