Authors :
Ibrahim Almubark
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/bdzhbr6b
Scribd :
https://tinyurl.com/4b2s4v4r
DOI :
https://doi.org/10.5281/zenodo.10423567
Abstract :
The capacity to predict the seemingly
ambiguous transition from mild cognitive impairment
(MCI) to progressive cognitive decline is a critical
concern in cognitive research. Advancement in
computational systems has contributed to more robust
potential to apply innovations in this sector. This study
uses a multilayer perceptron (MLP) neural network
approach to investigate and compare the utility of various
neuropsychological tests to predict a 3-year progression
from MCI. The MLP neural network is developed using
the open database from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI). The data were based on
a sample of 246 subjects with MCI whose diagnostic
follow-up was available for at least the full 3-year period
after the initial baseline assessment during the initial
project period, i.e., ADNI-1. Classification results and
analysis demonstrated that the combined features from
all three neuropsychological tests outperformed a single
test and the pairwise tests with an accuracy of 89.43%, a
sensitivity of 89.19%, a specificity of 89.63%, and the
area under the receiver operating characteristic curve
(AUC) of 0.934.
Keywords :
Mild Cognitive Impairment; Artificial Neural Network; Neuropsychological Testing.
The capacity to predict the seemingly
ambiguous transition from mild cognitive impairment
(MCI) to progressive cognitive decline is a critical
concern in cognitive research. Advancement in
computational systems has contributed to more robust
potential to apply innovations in this sector. This study
uses a multilayer perceptron (MLP) neural network
approach to investigate and compare the utility of various
neuropsychological tests to predict a 3-year progression
from MCI. The MLP neural network is developed using
the open database from the Alzheimer’s Disease
Neuroimaging Initiative (ADNI). The data were based on
a sample of 246 subjects with MCI whose diagnostic
follow-up was available for at least the full 3-year period
after the initial baseline assessment during the initial
project period, i.e., ADNI-1. Classification results and
analysis demonstrated that the combined features from
all three neuropsychological tests outperformed a single
test and the pairwise tests with an accuracy of 89.43%, a
sensitivity of 89.19%, a specificity of 89.63%, and the
area under the receiver operating characteristic curve
(AUC) of 0.934.
Keywords :
Mild Cognitive Impairment; Artificial Neural Network; Neuropsychological Testing.
