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Diagnosing Parkinson's Diseases Using Fuzzy Neural System
Rahib H Abiyev1, Sanan Abizade2
1Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey.
Computational and Mathematical Methods in Medicine
|February 17, 2016
Summary
This study introduces a novel fuzzy neural system (FNS) for Parkinson's disease diagnosis. The FNS effectively distinguishes between healthy individuals and Parkinson's patients, improving recognition rates.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Medicine
- Computational Neuroscience
Background:
- Parkinson's disease (PD) diagnosis relies on clinical symptoms, often leading to delayed detection.
- Developing accurate and early diagnostic tools is crucial for effective PD management.
- Computational methods offer potential for objective and non-invasive PD assessment.
Purpose of the Study:
- To design and evaluate a novel recognition system for discriminating between healthy individuals and Parkinson's disease patients.
- To present the structure and learning algorithms of a proposed fuzzy neural system (FNS).
- To enhance the diagnostic capability and recognition rate for Parkinson's disease detection.
Main Methods:
- Fusion of fuzzy systems and neural networks to create a fuzzy neural system (FNS).
- Development of specific structure and learning algorithms for the FNS.
- System simulation and validation using data from the UCI machine learning repository.
Main Results:
- The proposed fuzzy neural system (FNS) demonstrated effective discrimination between healthy and Parkinson's disease subjects.
- Simulation results confirmed the enhanced capability of the FNS in distinguishing individuals.
- Comparative analysis showed a significant improvement in the recognition rate using the FNS.
Conclusions:
- The developed fuzzy neural system (FNS) provides an effective approach for Parkinson's disease recognition.
- The FNS architecture and algorithms contribute to improved diagnostic accuracy.
- This computational approach holds promise for early and reliable detection of Parkinson's disease.
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