Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Abstract

Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alzheimer's disease is beneficial for its prevention and early intervention treatment. In this study, we propose a novel framework, FusionNet-ISBOA-MK-SVM, which integrates a fusion network (FusionNet) and improved secretary bird optimization algorithm to optimize multikernel support vector machine for Alzheimer's disease diagnosis. The model leverages multimodality data, including functional magnetic resonance imaging and genetic information (single-nucleotide polymorphisms). Specifically, FusionNet employs U-shaped hierarchical graph convolutional networks and sparse graph attention networks to select feature effectively. Extensive validation using the Alzheimer's Disease Neuroimaging Initiative dataset demonstrates the model's superior interpretability and classification performance. Compared to other state-of-the-art machine learning methods, FusionNet-ISBOA-MK-SVM achieves classification accuracies of 98.6%, 95.7%, 93.0%, 91.8%, 93.1%, and 95.4% for HC vs. AD, EMCI vs. AD, LMCI vs. AD, EMCI vs. AD, HC vs. EMCI, and HC vs. LMCI, respectively. Moreover, the proposed model identifies affected brain regions and pathogenic genes, offering deeper insights into the mechanisms and progression of Alzheimer's disease. These findings provide valuable scientific evidence to support early diagnosis and preventive strategies for Alzheimer's disease.