Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines
Main Author: | |
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Publication Date: | 2021 |
Other Authors: | , , , , , |
Format: | Article |
Language: | eng |
Source: | Repositório Institucional da UNESP |
Download full: | http://dx.doi.org/10.1016/j.compbiomed.2021.104260 http://hdl.handle.net/11449/210175 |
Summary: | Parkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals. Moreover, the most recent studies proposing a solution to this problem lack in computational efficiency, prediction power, reliability among other factors. Therefore, this work proposes a Fuzzy Optimum Path Forest for automated PD identification, which is based on fuzzy logic and graph-based framework theory. Experiments consider a dataset composed of features extracted from hand-drawn images using Restricted Boltzmann Machines, and results are compared with baseline models such as Support Vector Machines, KNN, and the standard OPF classifier. Results show that the proposed model outperforms the baselines in most cases, suggesting the Fuzzy OPF as a viable alternative to deal with PD detection problems. |
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Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann MachinesParkinson's diseaseFuzzy optimum-path forestMachine learningParkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals. Moreover, the most recent studies proposing a solution to this problem lack in computational efficiency, prediction power, reliability among other factors. Therefore, this work proposes a Fuzzy Optimum Path Forest for automated PD identification, which is based on fuzzy logic and graph-based framework theory. Experiments consider a dataset composed of features extracted from hand-drawn images using Restricted Boltzmann Machines, and results are compared with baseline models such as Support Vector Machines, KNN, and the standard OPF classifier. Results show that the proposed model outperforms the baselines in most cases, suggesting the Fuzzy OPF as a viable alternative to deal with PD detection problems.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Univ Fortaleza, Grad Program Appl Informat, Ave Washington Soares 1321, BR-60811905 Fortaleza, Ceara, BrazilSao Paulo State Univ, Dept Comp, Ave Engn Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, BrazilUniv Fed Ceara, Grad Program Teleinformat Engn, Fortaleza, Ceara, BrazilSao Paulo State Univ, Dept Comp, Ave Engn Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP, BrazilCNPq: 304315/2017-6CNPq: 430274/2018-1FAPESP: 2020/12101-0FAPESP: 2019/078251Elsevier B.V.Univ FortalezaUniversidade Estadual Paulista (Unesp)Univ Fed CearaSouza, Renato W. R. deSilva, Daniel S.Passos, Leandro A. [UNESP]Roder, Mateus [UNESP]Santana, Marcos C. [UNESP]Pinheiro, Placido R.Albuquerque, Victor Hugo C. de2021-06-25T12:41:59Z2021-06-25T12:41:59Z2021-04-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11http://dx.doi.org/10.1016/j.compbiomed.2021.104260Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 131, 11 p., 2021.0010-4825http://hdl.handle.net/11449/21017510.1016/j.compbiomed.2021.104260WOS:000634814300001Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengComputers In Biology And Medicineinfo:eu-repo/semantics/openAccess2024-04-23T16:10:43Zoai:repositorio.unesp.br:11449/210175Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:10:43Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
title |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
spellingShingle |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines Souza, Renato W. R. de Parkinson's disease Fuzzy optimum-path forest Machine learning |
title_short |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
title_full |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
title_fullStr |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
title_full_unstemmed |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
title_sort |
Computer-assisted Parkinson's disease diagnosis using fuzzy optimum- path forest and Restricted Boltzmann Machines |
author |
Souza, Renato W. R. de |
author_facet |
Souza, Renato W. R. de Silva, Daniel S. Passos, Leandro A. [UNESP] Roder, Mateus [UNESP] Santana, Marcos C. [UNESP] Pinheiro, Placido R. Albuquerque, Victor Hugo C. de |
author_role |
author |
author2 |
Silva, Daniel S. Passos, Leandro A. [UNESP] Roder, Mateus [UNESP] Santana, Marcos C. [UNESP] Pinheiro, Placido R. Albuquerque, Victor Hugo C. de |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Univ Fortaleza Universidade Estadual Paulista (Unesp) Univ Fed Ceara |
dc.contributor.author.fl_str_mv |
Souza, Renato W. R. de Silva, Daniel S. Passos, Leandro A. [UNESP] Roder, Mateus [UNESP] Santana, Marcos C. [UNESP] Pinheiro, Placido R. Albuquerque, Victor Hugo C. de |
dc.subject.por.fl_str_mv |
Parkinson's disease Fuzzy optimum-path forest Machine learning |
topic |
Parkinson's disease Fuzzy optimum-path forest Machine learning |
description |
Parkinson's disease (PD) is a progressive neurodegenerative illness associated with motor skill disorders, affecting thousands of people, mainly elderly, worldwide. Since its symptoms are not clear and commonly confused with other diseases, providing early diagnosis is a challenging task for traditional methods. In this context, computer-aided assistance is an alternative method for a fast and automatic diagnosis, accelerating the treatment and alleviating an excessive effort from professionals. Moreover, the most recent studies proposing a solution to this problem lack in computational efficiency, prediction power, reliability among other factors. Therefore, this work proposes a Fuzzy Optimum Path Forest for automated PD identification, which is based on fuzzy logic and graph-based framework theory. Experiments consider a dataset composed of features extracted from hand-drawn images using Restricted Boltzmann Machines, and results are compared with baseline models such as Support Vector Machines, KNN, and the standard OPF classifier. Results show that the proposed model outperforms the baselines in most cases, suggesting the Fuzzy OPF as a viable alternative to deal with PD detection problems. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-06-25T12:41:59Z 2021-06-25T12:41:59Z 2021-04-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1016/j.compbiomed.2021.104260 Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 131, 11 p., 2021. 0010-4825 http://hdl.handle.net/11449/210175 10.1016/j.compbiomed.2021.104260 WOS:000634814300001 |
url |
http://dx.doi.org/10.1016/j.compbiomed.2021.104260 http://hdl.handle.net/11449/210175 |
identifier_str_mv |
Computers In Biology And Medicine. Oxford: Pergamon-elsevier Science Ltd, v. 131, 11 p., 2021. 0010-4825 10.1016/j.compbiomed.2021.104260 WOS:000634814300001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Computers In Biology And Medicine |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
11 |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
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1797789356959727616 |