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Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster
Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it i...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186825/ https://www.ncbi.nlm.nih.gov/pubmed/30349454 http://dx.doi.org/10.3389/fnins.2018.00716 |
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author | Bi, Xia-an Xu, Qian Luo, Xianhao Sun, Qi Wang, Zhigang |
author_facet | Bi, Xia-an Xu, Qian Luo, Xianhao Sun, Qi Wang, Zhigang |
author_sort | Bi, Xia-an |
collection | PubMed |
description | Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development. |
format | Online Article Text |
id | pubmed-6186825 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61868252018-10-22 Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster Bi, Xia-an Xu, Qian Luo, Xianhao Sun, Qi Wang, Zhigang Front Neurosci Neuroscience Alzheimer’s disease (AD) could be described into following four stages: healthy control (HC), early mild cognitive impairment (EMCI), late MCI (LMCI) and AD dementia. The discriminations between different stages of AD are considerably important issues for future pre-dementia treatment. However, it is still challenging to identify LMCI from EMCI because of the subtle changes in imaging which are not noticeable. In addition, there were relatively few studies to make inferences about the brain dynamic changes in the cognitive progression from EMCI to LMCI to AD. Inspired by the above problems, we proposed an advanced approach of evolutionary weighted random support vector machine cluster (EWRSVMC). Where the predictions of numerous weighted SVM classifiers are aggregated for improving the generalization performance. We validated our method in multiple binary classifications using Alzheimer’s Disease Neuroimaging Initiative dataset. As a result, the encouraging accuracy of 90% for EMCI/LMCI and 88.89% for LMCI/AD were achieved respectively, demonstrating the excellent discriminating ability. Furthermore, disease-related brain regions underlying the AD progression could be found out on the basis of the amount of discriminative information. The findings of this study provide considerable insight into the neurophysiological mechanisms in AD development. Frontiers Media S.A. 2018-10-08 /pmc/articles/PMC6186825/ /pubmed/30349454 http://dx.doi.org/10.3389/fnins.2018.00716 Text en Copyright © 2018 Bi, Xu, Luo, Sun and Wang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Bi, Xia-an Xu, Qian Luo, Xianhao Sun, Qi Wang, Zhigang Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster |
title | Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster |
title_full | Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster |
title_fullStr | Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster |
title_full_unstemmed | Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster |
title_short | Analysis of Progression Toward Alzheimer’s Disease Based on Evolutionary Weighted Random Support Vector Machine Cluster |
title_sort | analysis of progression toward alzheimer’s disease based on evolutionary weighted random support vector machine cluster |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6186825/ https://www.ncbi.nlm.nih.gov/pubmed/30349454 http://dx.doi.org/10.3389/fnins.2018.00716 |
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