Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm
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 o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/6/3048 |
| _version_ | 1827650065051680768 |
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| author | Ignacio Rojas-Valenzuela Olga Valenzuela Elvira Delgado-Marquez Fernando Rojas |
| author_facet | Ignacio Rojas-Valenzuela Olga Valenzuela Elvira Delgado-Marquez Fernando Rojas |
| author_sort | Ignacio Rojas-Valenzuela |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T20:09:15Z |
| format | Article |
| id | doaj.art-f925e199ce4846cfb916810deb084593 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T20:09:15Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f925e199ce4846cfb916810deb0845932023-11-24T00:22:56ZengMDPI AGApplied Sciences2076-34172022-03-01126304810.3390/app12063048Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization AlgorithmIgnacio Rojas-Valenzuela0Olga Valenzuela1Elvira Delgado-Marquez2Fernando Rojas3School of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, SpainDepartment of Applied Mathematics, University of Granada, 18071 Granada, SpainDepartment of Economics and Statistics, University of Leon, 24004 Leon, SpainSchool of Technology and Telecommunications Engineering, University of Granada, 18071 Granada, SpainIn 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.https://www.mdpi.com/2076-3417/12/6/3048Parkinson’s disease (PD)3D-discrete wavelet transform (3D-DWT)support vector machine (SVM)multi-objective optimization evolutionary algorithm (MOE)minimum redundancy maximum relevance (mRMR) |
| spellingShingle | Ignacio Rojas-Valenzuela Olga Valenzuela Elvira Delgado-Marquez Fernando Rojas Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm Applied Sciences Parkinson’s disease (PD) 3D-discrete wavelet transform (3D-DWT) support vector machine (SVM) multi-objective optimization evolutionary algorithm (MOE) minimum redundancy maximum relevance (mRMR) |
| title | Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm |
| title_full | Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm |
| title_fullStr | Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm |
| title_full_unstemmed | Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm |
| title_short | Multi-Class Classifier in Parkinson’s Disease Using an Evolutionary Multi-Objective Optimization Algorithm |
| title_sort | multi class classifier in parkinson s disease using an evolutionary multi objective optimization algorithm |
| topic | Parkinson’s disease (PD) 3D-discrete wavelet transform (3D-DWT) support vector machine (SVM) multi-objective optimization evolutionary algorithm (MOE) minimum redundancy maximum relevance (mRMR) |
| url | https://www.mdpi.com/2076-3417/12/6/3048 |
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