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A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study
The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some fe...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177228/ https://www.ncbi.nlm.nih.gov/pubmed/35693342 http://dx.doi.org/10.3389/fnagi.2022.888575 |
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author | Lu, Jia Zeng, Weiming Zhang, Lu Shi, Yuhu |
author_facet | Lu, Jia Zeng, Weiming Zhang, Lu Shi, Yuhu |
author_sort | Lu, Jia |
collection | PubMed |
description | The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy. |
format | Online Article Text |
id | pubmed-9177228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91772282022-06-09 A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study Lu, Jia Zeng, Weiming Zhang, Lu Shi, Yuhu Front Aging Neurosci Neuroscience The Extreme Learning Machine (ELM) is a simple and efficient Single Hidden Layer Feedforward Neural Network(SLFN) algorithm. In recent years, it has been gradually used in the study of Alzheimer’s disease (AD). When using ELM to diagnose AD based on high-dimensional features, there are often some features that have no positive impact on the diagnosis, while others have a significant impact on the diagnosis. In this paper, a novel Key Features Screening Method based on Extreme Learning Machine (KFS-ELM) is proposed. It can screen for key features that are relevant to the classification (diagnosis). It can also assign weights to key features based on their importance. We designed an experiment to screen for key features of AD. A total of 920 key functional connections screened from 4005 functional connections. Their weights were also obtained. The results of the experiment showed that: (1) Using all (4,005) features to diagnose AD, the accuracy is 95.33%. Using 920 key features to diagnose AD, the accuracy is 99.20%. The 3,085 (4,005 - 920) features that were screened out had a negative effect on the diagnosis of AD. This indicates the KFS-ELM is effective in screening key features. (2) The higher the weight of the key features and the smaller their number, the greater their impact on AD diagnosis. This indicates that the KFS-ELM is rational in assigning weights to the key features for their importance. Therefore, KFS-ELM can be used as a tool for studying features and also for improving classification accuracy. Frontiers Media S.A. 2022-05-25 /pmc/articles/PMC9177228/ /pubmed/35693342 http://dx.doi.org/10.3389/fnagi.2022.888575 Text en Copyright © 2022 Lu, Zeng, Zhang and Shi. https://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 Lu, Jia Zeng, Weiming Zhang, Lu Shi, Yuhu A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study |
title | A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study |
title_full | A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study |
title_fullStr | A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study |
title_full_unstemmed | A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study |
title_short | A Novel Key Features Screening Method Based on Extreme Learning Machine for Alzheimer’s Disease Study |
title_sort | novel key features screening method based on extreme learning machine for alzheimer’s disease study |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9177228/ https://www.ncbi.nlm.nih.gov/pubmed/35693342 http://dx.doi.org/10.3389/fnagi.2022.888575 |
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