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Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach

The study of brain connectivity patterns reveal important information in the understanding of the brain's functional organization. Resting-state functional magnetic resonance imaging (rs-fMRI) is a type of neuroimaging technique that can be used to diagnose a variety of neurological conditions....

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Main Authors: Sadiq, A., Yahya, N., Tang, T.B.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125770255&doi=10.1109%2fDASA53625.2021.9682409&partnerID=40&md5=e91525edd19250503d05b14cabb34e89
http://eprints.utp.edu.my/29122/
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spelling my.utp.eprints.291222022-03-25T00:57:52Z Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach Sadiq, A. Yahya, N. Tang, T.B. The study of brain connectivity patterns reveal important information in the understanding of the brain's functional organization. Resting-state functional magnetic resonance imaging (rs-fMRI) is a type of neuroimaging technique that can be used to diagnose a variety of neurological conditions. In this study, Pearson's correlation connectivity (PCC) and the feature selection algorithm ReliefF are used to distinguish Alzheimer's disease (AD) patients from normal controls (NC). PCC is a common measure to find the correlation between regions and ReliefF is known to perform well with high dimensional feature vectors so the combination of two gives a good accuracy. Using a k-nearest neighbor (KNN) classifier, the proposed method achieved a classification accuracy of 93.5 percent, showing the good potential of the proposed approach. © 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125770255&doi=10.1109%2fDASA53625.2021.9682409&partnerID=40&md5=e91525edd19250503d05b14cabb34e89 Sadiq, A. and Yahya, N. and Tang, T.B. (2021) Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach. In: UNSPECIFIED. http://eprints.utp.edu.my/29122/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The study of brain connectivity patterns reveal important information in the understanding of the brain's functional organization. Resting-state functional magnetic resonance imaging (rs-fMRI) is a type of neuroimaging technique that can be used to diagnose a variety of neurological conditions. In this study, Pearson's correlation connectivity (PCC) and the feature selection algorithm ReliefF are used to distinguish Alzheimer's disease (AD) patients from normal controls (NC). PCC is a common measure to find the correlation between regions and ReliefF is known to perform well with high dimensional feature vectors so the combination of two gives a good accuracy. Using a k-nearest neighbor (KNN) classifier, the proposed method achieved a classification accuracy of 93.5 percent, showing the good potential of the proposed approach. © 2021 IEEE.
format Conference or Workshop Item
author Sadiq, A.
Yahya, N.
Tang, T.B.
spellingShingle Sadiq, A.
Yahya, N.
Tang, T.B.
Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
author_facet Sadiq, A.
Yahya, N.
Tang, T.B.
author_sort Sadiq, A.
title Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
title_short Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
title_full Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
title_fullStr Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
title_full_unstemmed Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
title_sort diagnosis of alzheimer's disease using pearson's correlation and relieff feature selection approach
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125770255&doi=10.1109%2fDASA53625.2021.9682409&partnerID=40&md5=e91525edd19250503d05b14cabb34e89
http://eprints.utp.edu.my/29122/
_version_ 1738656920995102720
score 13.211869

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