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

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...

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Main Authors:	Al Sayaydeh, Osama Nayel, Mohammed, Mohammed Falah
Format:	Conference or Workshop Item
Language:	English
Published:	2019
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/24055/ http://umpir.ump.edu.my/id/eprint/24055/1/A%20New%20EFMM-OneR%20Hybrid%20Model%20for%20Diagnosing%20Parkinson%27s%20Disease1.pdf

id	ump-24055
recordtype	eprints
spelling	ump-240552019-06-10T07:03:08Z http://umpir.ump.edu.my/id/eprint/24055/ A new EFMM-OneR hybrid model for diagnosing parkinson's disease Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah QA75 Electronic computers. Computer science 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. 2019-01-25 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/24055/1/A%20New%20EFMM-OneR%20Hybrid%20Model%20for%20Diagnosing%20Parkinson%27s%20Disease1.pdf 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)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah A new EFMM-OneR hybrid model for diagnosing parkinson's disease
description	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.
format	Conference or Workshop Item
author	Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah
author_facet	Al Sayaydeh, Osama Nayel Mohammed, Mohammed Falah
author_sort	Al Sayaydeh, Osama Nayel
title	A new EFMM-OneR hybrid model for diagnosing parkinson's disease
title_short	A new EFMM-OneR hybrid model for diagnosing parkinson's disease
title_full	A new EFMM-OneR hybrid model for diagnosing parkinson's disease
title_fullStr	A new EFMM-OneR hybrid model for diagnosing parkinson's disease
title_full_unstemmed	A new EFMM-OneR hybrid model for diagnosing parkinson's disease
title_sort	new efmm-oner hybrid model for diagnosing parkinson's disease
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/24055/ http://umpir.ump.edu.my/id/eprint/24055/1/A%20New%20EFMM-OneR%20Hybrid%20Model%20for%20Diagnosing%20Parkinson%27s%20Disease1.pdf
first_indexed	2023-09-18T22:36:14Z
last_indexed	2023-09-18T22:36:14Z
_version_	1777416614442958848

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

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