  The timely identification of significant memory concern (SMC) is crucial for
proactive cognitive health management, especially in an aging population.
Detecting SMC early enables timely intervention and personalized care,
potentially slowing cognitive disorder progression. This study presents a
state-of-the-art review followed by a comprehensive evaluation of machine
learning models within the randomized neural networks (RNNs) and
hyperplane-based classifiers (HbCs) family to investigate SMC diagnosis
thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2)
dataset, 111 individuals with SMC and 111 healthy older adults are analyzed
based on T1W magnetic resonance imaging (MRI) scans, extracting rich features.
This analysis is based on baseline structural MRI (sMRI) scans, extracting rich
features from gray matter (GM), white matter (WM), Jacobian determinant (JD),
and cortical thickness (CT) measurements. In RNNs, deep random vector
functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best
classifiers in terms of performance metrics in the identification of SMC. In
HbCs, Kernelized pinball general twin support vector machine (Pin-GTSVM-K)
excels in CT and WM features, whereas Linear Pin-GTSVM (Pin-GTSVM-L) and Linear
intuitionistic fuzzy TSVM (IFTSVM-L) performs well in the JD and GM features
sets, respectively. This comprehensive evaluation emphasizes the critical role
of feature selection and model choice in attaining an effective classifier for
SMC diagnosis. The inclusion of statistical analyses further reinforces the
credibility of the results, affirming the rigor of this analysis. The
performance measures exhibit the suitability of this framework in aiding
researchers with the automated and accurate assessment of SMC. The source codes
of the algorithms and datasets used in this study are available at
https://github.com/mtanveer1/SMC.
