Early Mild Cognitive Impairment (EMCI) is a transitional phase
between normal cognition (NC) and Alzheimer’s disease. Accurate
detection of EMCI can be challenging due to its subtle manifestations.
Traditional methods often struggle to differentiate EMCI from NC
using neuropsychological tests alone, necessitating advanced
techniques for effective classification. We employed Ensemble
DenseNets to cluster a multi-modal dataset comprising
neuropsychological tests and clinical data. Generalized Estimating
Equations (GEE) were used to analyze changes over time across
various cognitive tests. Our model demonstrated significant findings:
MMSE showed a time effect (ß = 0.151, p = 0.01) with a notable decline
in EMCI compared to NC (ß = -0.299, p = 0.001). STM also showed
significant results (time ß = 0.105, p < 0.001). In the CVVLT total recall
test, a time effect (ß = 1.263, p < 0.001) and a decline in EMCI (ß = -
0.510, p = 0.003) were observed. The method effectively clustered
EMCI with a high degree of accuracy, showcasing the robustness of
Ensemble DenseNets for early detection.
A.S. Shanthi
Dr. N.G.P. Institute of Technology, India
Early Mild Cognitive Impairment, Ensemble DenseNets, Neuropsychological Tests, Generalized Estimating Equations, Cognitive Clustering
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 15 , Issue: 1 , Pages: 3442 - 3451 )
Date of Publication :
July 2024
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170
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