Comorbidity-based framework for Alzheimer’s disease classification using graph neural networks

• Published in: Scientific reports 2024-09, Vol.14 (1), p.21061-21, Article 21061
• Main Authors: Abuhantash, Ferial, Abu Hantash, Mohd Khalil, AlShehhi, Aamna
• Format: Article
• Language: English
• Subjects: 639/705; 692/308; 692/499; 692/53; 692/53/2421; 692/699; 692/700; 692/700/139; Aged; Aged, 80 and over; Alzheimer Disease - classification; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - epidemiology; Alzheimer's disease; Classification; Cognitive ability; Cognitive Dysfunction - classification; Cognitive Dysfunction - diagnosis; Cognitive Dysfunction - epidemiology; Comorbidity; Deep Learning; Dementia disorders; Electronic medical records; Female; Humanities and Social Sciences; Humans; Male; multidisciplinary; Neural networks; Neural Networks, Computer; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-72321-2

Alzheimer’s disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Current deep learning approaches, particularly those using traditional neural networks, face challenges such as handling high-dimensional data, interpreting complex relationships, and managing data bias. To address these limitations, we propose a framework utilizing graph neural networks (GNNs), which excel in modeling relationships within graph-structured data. Our study employs GNNs on data from the Alzheimer’s Disease Neuroimaging Initiative for binary and multi-class classification across the three stages of AD: cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD). By incorporating comorbidity data derived from electronic health records, we achieved the most effective multi-classification results. Notably, the GNN model (Chebyshev Convolutional Neural Networks) demonstrated superior performance with a 0.98 accuracy in multi-class classification and 0.99, 0.93, and 0.94 in the AD/CN, AD/MCI, and CN/MCI binary tasks, respectively. The model’s robustness was further validated using the Australian Imaging, Biomarker & Lifestyle dataset as an external validation set. This work contributes to the field by offering a robust, accurate, and cost-effective method for early AD prediction (CN vs. MCI), addressing key challenges in existing deep learning approaches.