Publication Date: 2023/12/04
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.
DOI: https://doi.org/10.5281/zenodo.10423567
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV1370.pdf
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