TY - GEN
T1 - Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks
AU - Abuhantash, Ferial
AU - Abu Hantash, Mohd Khalil
AU - Welsch, Roy
AU - Seghier, Mohamed Lamine
AU - Hadjileontiadis, Leontios
AU - Al Shehhi, Aamna
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-class AD classification. Initial steps involve creating a patient-clinical graph network considering latent relationships among cognitive normal (CN), mild cognitive impairment (MCI), and AD patients, followed by training several GNN-based techniques for building prediction models. Incorporating comorbidity data from electronic health records into the feature set yielded the most effective classification results. Notably, the GNN model with attention mechanisms outperforms state-of-the-art techniques in multi-class AD classification, achieving an accuracy = 0.92 [0.91,0.94], AUC = 0.96 [0.95,0.96], and F1-score = 0.92 [0.91,0.94]. This work highlights comorbidity data's impact on AD classification and suggests its potential to deepen disease understanding.
AB - Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our study employs Graph Neural Networks (GNNs) for multi-class AD classification. Initial steps involve creating a patient-clinical graph network considering latent relationships among cognitive normal (CN), mild cognitive impairment (MCI), and AD patients, followed by training several GNN-based techniques for building prediction models. Incorporating comorbidity data from electronic health records into the feature set yielded the most effective classification results. Notably, the GNN model with attention mechanisms outperforms state-of-the-art techniques in multi-class AD classification, achieving an accuracy = 0.92 [0.91,0.94], AUC = 0.96 [0.95,0.96], and F1-score = 0.92 [0.91,0.94]. This work highlights comorbidity data's impact on AD classification and suggests its potential to deepen disease understanding.
KW - Alzheimer's Disease
KW - Clinical Data
KW - Comorbidity
KW - Multi-class Classification
UR - https://www.scopus.com/pages/publications/85214987721
U2 - 10.1109/EMBC53108.2024.10781747
DO - 10.1109/EMBC53108.2024.10781747
M3 - Conference contribution
C2 - 40039558
AN - SCOPUS:85214987721
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
BT - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Y2 - 15 July 2024 through 19 July 2024
ER -