  Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that
primarily affects the aging population by impairing cognitive and motor
functions. Early detection of AD through accessible methodologies like magnetic
resonance imaging (MRI) is vital for developing effective interventions to halt
or slow the disease's progression. This study aims to perform a comprehensive
analysis of machine learning techniques for selecting MRI-based biomarkers and
classifying individuals into healthy controls (HC) and unstable controls (uHC)
who later show mild cognitive impairment within five years. The research
utilizes MRI data from the Alzheimer's Disease Neuroinformatics Initiative
(ADNI) and the Open Access Series of Imaging Studies 3 (OASIS-3), focusing on
both HC and uHC participants. The study addresses the challenges of imbalanced
data by testing classification methods on balanced and unbalanced datasets, and
harmonizes data using polynomial regression to mitigate nuisance variables like
age, gender, and intracranial volume. Results indicate that Gaussian Naive
Bayes and RusBoost classifiers shows an optimal performance, achieving
accuracies of up to 76.46% and 72.48% respectively on the ADNI dataset. For the
OASIS-3 dataset, Kernel Naive Bayes and RusBoost yield accuracies ranging from
64.66% to 75.71%, improving further in age-matched datasets. Brain regions like
the entorhinal cortex, hippocampus, lateral ventricle, and lateral
orbitofrontal cortex are identified as significantly impacted during early
cognitive decline. Despite limitations such as small sample sizes, the study's
harmonization approach enhances the robustness of biomarker selection,
suggesting the potential of this semi-automatic machine learning pipeline for
early AD detection using MRI.
