Mostrar el registro sencillo del ítem

dc.contributor.authorRojas Valenzuela, Ignacio
dc.contributor.authorValenzuela Cansino, Olga 
dc.contributor.authorRojas Ruiz, Fernando José 
dc.date.accessioned2022-04-20T07:27:25Z
dc.date.available2022-04-20T07:27:25Z
dc.date.issued2022-03-16
dc.identifier.citationRojas-Valenzuela, I... [et al.]. Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm. Appl. Sci. 2022, 12, 3048. [https://doi.org/10.3390/app12063048]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/74386
dc.descriptionThis work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Project RTI-2018-101674-B-I00 and the projects from Junta de Andalucia B-TIC-414, A-TIC-530-UGR20 and P20-00163.es_ES
dc.description.abstractIn this contribution, a novel methodology for multi-class classification in the field of Parkinson’s disease is proposed. The methodology is structured in two phases. In a first phase, the most relevant volumes of interest (VOI) of the brain are selected by means of an evolutionary multi-objective optimization (MOE) algorithm. Each of these VOIs are subjected to volumetric feature extraction using the Three-Dimensional Discrete Wavelet Transform (3D-DWT). When applying 3D-DWT, a high number of coefficients is obtained, requiring the use of feature selection/reduction algorithms to find the most relevant features. The method used in this contribution is based on Mutual Redundancy (MI) and Minimum Maximum Relevance (mRMR) and PCA. To optimize the VOI selection, a first group of 550 MRI was used for the 5 classes: PD, SWEDD, Prodromal, GeneCohort and Normal. Once the Pareto Front of the solutions is obtained (with varying degrees of complexity, reflected in the number of selected VOIs), these solutions are tested in a second phase. In order to analyze the SVM classifier accuracy, a test set of 367 MRI was used. The methodology obtains relevant results in multi-class classification, presenting several solutions with different levels of complexity and precision (Pareto Front solutions), reaching a result of 97% as the highest precision in the test data.es_ES
dc.description.sponsorshipSpanish Government RTI-2018-101674-B-I00es_ES
dc.description.sponsorshipJunta de Andalucia B-TIC-414 A-TIC-530-UGR20 P20-00163es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectParkinson's disease (PD)es_ES
dc.subject3D-discrete wavelet transform (3D-DWT)es_ES
dc.subjectSupport Vector Machine (SVM)es_ES
dc.subjectMulti-objective optimization evolutionary algorithm (MOE)es_ES
dc.subjectMinimum redundancy maximum relevance (mRMR)es_ES
dc.titleMulti-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/app12063048
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


Ficheros en el ítem

[PDF]

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem

Atribución 3.0 España
Excepto si se señala otra cosa, la licencia del ítem se describe como Atribución 3.0 España