Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder marked by structural brain changes detectable through neuroimaging, particularly Magnetic Resonance Imaging (MRI), which can reveal the extent of atrophy in cortical and subcortical regions. We propose an ensemble of linear models for AD classification, combining regression and classification techniques for a novel methodology to identify potential biomarkers from MRI data, termed Apparent Brain Features (ABF). These biomarkers represent morphological brain regions automatically selected to optimise classification accuracy while preserving interpretability. Unlike deep learning or other nonlinear methods, our approach maintains the anatomical semantics of the input space. A key innovation is a feature score that quantifies the influence of each selected morphological region on classification, enabling both diagnostic utility and neuroscientific insights. We validate our approach on MRI scans from 1990 subjects gathered from four publicly available repositories: ADNI, AIBL, PPMI, and IXI. The results show that our ensemble methodology achieves high classification accuracy while offering an interpretable framework for assessing the role of brain morphology in AD. A systematic selection and evaluation of brain regions can provide a transparent and clinically relevant tool, supporting both computational neuroscience research and practical diagnostic applications.