Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm

Parkinson’s disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes a...

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Main Authors: Musa Peker, Baha Sen, Dursun Delen
Format: Article
Language:English
Published: Hindawi Limited 2015-01-01
Series:Journal of Healthcare Engineering
Online Access:http://dx.doi.org/10.1260/2040-2295.6.3.281
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spelling doaj-992dcba0b06f422e9eabebb70a9cc8272020-11-24T22:32:13ZengHindawi LimitedJournal of Healthcare Engineering2040-22952015-01-016328130210.1260/2040-2295.6.3.281Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection AlgorithmMusa Peker0Baha Sen1Dursun Delen2Department of Information Technology, Samandira Technical and Vocational High Schools, Sancaktepe, Istanbul, TurkeyYıldırım Beyazıt University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Ankara, TurkeyDepartment of Management Science and Information Systems, Oklahoma State University, Stillwater, Oklahoma, USAParkinson’s disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.http://dx.doi.org/10.1260/2040-2295.6.3.281
collection DOAJ
language English
format Article
sources DOAJ
author Musa Peker
Baha Sen
Dursun Delen
spellingShingle Musa Peker
Baha Sen
Dursun Delen
Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
Journal of Healthcare Engineering
author_facet Musa Peker
Baha Sen
Dursun Delen
author_sort Musa Peker
title Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
title_short Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
title_full Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
title_fullStr Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
title_full_unstemmed Computer-Aided Diagnosis of Parkinson’s Disease Using Complex-Valued Neural Networks and mRMR Feature Selection Algorithm
title_sort computer-aided diagnosis of parkinson’s disease using complex-valued neural networks and mrmr feature selection algorithm
publisher Hindawi Limited
series Journal of Healthcare Engineering
issn 2040-2295
publishDate 2015-01-01
description Parkinson’s disease (PD) is a neurological disorder which has a significant social and economic impact. PD is diagnosed by clinical observation and evaluations, coupled with a PD rating scale. However, these methods may be insufficient, especially in the initial phase of the disease. The processes are tedious and time-consuming, and hence systems that can automatically offer a diagnosis are needed. In this study, a novel method for the diagnosis of PD is proposed. Biomedical sound measurements obtained from continuous phonation samples were used as attributes. First, a minimum redundancy maximum relevance (mRMR) attribute selection algorithm was applied for the identification of the effective attributes. After conversion to a complex number, the resulting attributes are presented as input data to the complex-valued artificial neural network (CVANN). The proposed novel system might be a powerful tool for effective diagnosis of PD.
url http://dx.doi.org/10.1260/2040-2295.6.3.281
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AT bahasen computeraideddiagnosisofparkinsonsdiseaseusingcomplexvaluedneuralnetworksandmrmrfeatureselectionalgorithm
AT dursundelen computeraideddiagnosisofparkinsonsdiseaseusingcomplexvaluedneuralnetworksandmrmrfeatureselectionalgorithm
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