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

Ferial Abuhantash1, Mohd Khalil Abu Hantash1, Aamna AlShehhi2,3

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Graph neural networks (GNNs) offer a powerful solution for early Alzheimer's disease (AD) prediction. This study demonstrates GNNs accurately classify disease stages using neuroimaging and comorbidity data.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Alzheimer's disease (AD) is the leading cause of dementia, necessitating early detection for effective intervention.
  • Traditional deep learning methods struggle with high-dimensional data, complex relationships, and bias in AD prediction.
  • Graph neural networks (GNNs) show promise for modeling relational data, offering a potential solution to these challenges.

Purpose of the Study:

  • To develop and evaluate a GNN-based framework for early prediction of Alzheimer's disease stages.
  • To compare the performance of GNNs against traditional deep learning approaches in AD classification.
  • To investigate the utility of incorporating comorbidity data into GNN models for enhanced AD prediction accuracy.

Main Methods:

  • Utilized GNNs, specifically Chebyshev Convolutional Neural Networks, for classification tasks.
  • Employed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for training and validation.
  • Incorporated comorbidity data from electronic health records alongside neuroimaging data.
  • Performed binary (AD/CN, AD/MCI, CN/MCI) and multi-class classification of cognitive states.
  • Validated the model's performance using the Australian Imaging, Biomarker & Lifestyle (AIBL) dataset.

Main Results:

  • The GNN model achieved high accuracy in multi-class classification (0.98) and binary classifications (AD/CN: 0.99, AD/MCI: 0.93, CN/MCI: 0.94).
  • Incorporating comorbidity data significantly improved multi-classification performance.
  • The model demonstrated robust performance on an independent external validation dataset (AIBL).

Conclusions:

  • GNNs provide a robust, accurate, and potentially cost-effective method for early Alzheimer's disease prediction.
  • The proposed GNN framework effectively addresses limitations of traditional deep learning models in handling complex AD data.
  • This approach shows particular promise for distinguishing between cognitively normal (CN) and mild cognitive impairment (MCI) stages, facilitating timely intervention.