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A new EFMM-OneR hybrid model for diagnosing parkinson's disease

Al Sayaydeh, Osama Nayel and Mohammed, Mohammed Falah (2019) A new EFMM-OneR hybrid model for diagnosing parkinson's disease. In: 2nd International Conference on Advanced Science and Engineering 2019 (ICOASE2019), 2-4 April 2019 , Duhok, Kurdistan Region-Iraq. pp. 1-6.. (Submitted)

A New EFMM-OneR Hybrid Model for Diagnosing Parkinson's Disease1.pdf

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Parkinson's disease is a dangerous disease that attacks the nervous system and affects it negatively over time. Early diagnosis of this disease is necessary for identifying the most appropriate treatment for preventing the disease from worsening. It can be diagnosed by examining the symptoms of the patient. Recently, researchers have used voice disorders to diagnose Parkinson's disease by extracting attributes from audio recordings of affected people and using classification techniques to provide accurate diagnoses. In this paper, an enhanced fuzzy min-max neural network based on the OneR attribute evaluator (EFMM-OneR) is proposed as a hybrid model for diagnosing Parkinson's disease. The hybrid model consists of two stages: In the first stage, feature selection is used to identify and remove irrelevant, redundant, or noisy features from the provided dataset. In the second stage, the enhanced fuzzy min-max (EFMM) neural network is used for the classification process. The results demonstrated the ability of the EFMM-OneR model to improve the classification accuracy as compared to other classifiers from the literature.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Fuzzy min–max; Pattern classification; Fuzzy neural network; Parkinson's disease
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Dr. Mohammed Falah Mohammed
Date Deposited: 10 Jun 2019 07:03
Last Modified: 10 Jun 2019 07:03
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