Abstract
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the aging global population, AD incidence and its socioeconomic burden are increasing. Developing effective early diagnostic methods is thus critical for improving patient outcomes and slowing disease progression. In this paper, an enhanced Particle Swarm Optimization (PSO) algorithm, which integrates opposition-based Latin squares sampling initialization (OL) with dynamic inertia weights and learning factors (D), termed OLDPSO, is proposed to improve feature selection and classification within a Support Vector Machine (SVM) model for AD diagnosis using magnetic resonance imaging (MRI) data. MRI, as a non-invasive modality, reveals structural brain changes, particularly in gray matter (GM) and white matter (WM) volumes, which are key biomarkers for AD. However, extracting essential features from complex GM and WM data remains a significant challenge. To address this, the proposed OLDPSO, which adaptively balances global exploration and local exploitation, overcomes traditional PSO limitations. Benchmark experiments show that OLDPSO outperforms existing PSO variants in solution quality and convergence speed. Validated with data from the AD Neuroimaging Initiative (ADNI), the OLDPSO-SVM model demonstrates superior performance in differentiating AD, mild cognitive impairment (MCI), and normal control (NC) groups, particularly in classifying MCI subtypes (MCI-NC and MCI-C). Results show that combining GM and WM features yields higher diagnostic accuracy than using either alone, and the model identified key brain regions associated with AD progression. Specifically, the model achieved accuracies of 99.11%, 89.76%, 99.07%, 88.38%, 94.69%, and 87.96% in the diagnosis of AD vs. NC, NC vs. MCI-NC, NC vs. MCI-C, MCI-NC vs. MCI-C, MCI-NC vs. AD, and MCI-C vs. AD, respectively. Through optimized feature selection, the OLDPSO-SVM model enhances diagnostic performance and provides valuable insights for developing MRI-based multimodal diagnostic tools for AD.