dc.contributor | Facultad de Ciencias Economicas y Empresariales | es_ES |
dc.contributor.author | Rojas Valenzuela, Ignacio | |
dc.contributor.author | Valenzuela, Olga | |
dc.contributor.author | Delgado Márquez, Elvira | |
dc.contributor.author | Rojas, Fernando | |
dc.contributor.other | Economia Aplicada | es_ES |
dc.date | 2022 | |
dc.date.accessioned | 2024-04-18T11:16:50Z | |
dc.date.available | 2024-04-18T11:16:50Z | |
dc.identifier.citation | Rojas-Valenzuela, I., Valenzuela, O., Delgado-Márquez, E., y Rojas, F. (2022). Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm. Applied Sciences , 12(6), 3048. https://doi.org/10.3390/APP12063048. | es_ES |
dc.identifier.other | https://www.mdpi.com/2076-3417/12/6/3048 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/19942 | |
dc.description.abstract | [EN] In 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.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Atribución 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Economía | es_ES |
dc.subject | Estadística | es_ES |
dc.subject.other | Parkinson’s disease (PD) | es_ES |
dc.subject.other | 3D-discrete wavelet transform (3D-DWT) | es_ES |
dc.subject.other | Support vector machine (SVM) | es_ES |
dc.subject.other | Multi-objective optimization evolutionary algorithm (MOE) | es_ES |
dc.subject.other | Minimum redundancy maximum relevance (mRMR) | es_ES |
dc.title | Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/app12063048 | |
dc.description.peerreviewed | SI | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 2076-3417 | |
dc.journal.title | Applied Sciences | es_ES |
dc.volume.number | 12 | es_ES |
dc.issue.number | 6 | es_ES |
dc.page.initial | 3048 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |