TY  - CONF
EP  - 582
A1  - Sadiq, A.
A1  - Yahya, N.
A1  - Tang, T.B.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125770255&doi=10.1109%2fDASA53625.2021.9682409&partnerID=40&md5=e91525edd19250503d05b14cabb34e89
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781665416344
Y1  - 2021///
ID  - scholars15389
TI  - Diagnosis of Alzheimer's Disease Using Pearson's Correlation and ReliefF Feature Selection Approach
SP  - 578
KW  - Computer aided diagnosis; Feature extraction; Magnetic resonance imaging; Nearest neighbor search; Neurodegenerative diseases; Neuroimaging
KW  -  Alzheimers disease; Brain connectivity; Condition; Connectivity pattern; Features selection; Functional organization; Neuroimaging techniques; Pearson correlation; ReliefF; Resting-state functional magnetic resonance imaging
KW  -  Correlation methods
N2  - 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.
N1  - cited By 3; Conference of 2021 International Conference on Decision Aid Sciences and Application, DASA 2021 ; Conference Date: 7 December 2021 Through 8 December 2021; Conference Code:176623
AV  - none
ER  -