Alzheimer’s Disease Classification Using Cluster-based Labelling for Graph Neural Network on Heterogeneous Data

Niamh Mc Combe, Jake Bamrah, Jose Sanchez-Bornot, David Finn, Paula McClean, KongFatt Wong-Lin

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Biomarkers for Alzheimer’s disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data-driven diagnostic classes from unsupervised clustering on heterogeneous data, is compared to the performance of a classifier using clinician diagnosis as outcome. Unsupervised clustering on tau-PET and cognitive and functional assessment data was performed. Five clusters embedded in a nonlinear UMAP space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors. In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re-labelled AD cases. The re-labelled cases are characterised by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re-labelled data with a multiclass area-under-the-curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; p=0.02). Overall, our work suggests that more objective cluster-based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.
Original languageEnglish
Number of pages8
JournalHealth Technology Letters
Early online date20 Oct 2022
Publication statusPublished online - 20 Oct 2022


  • patient diagnosis
  • neural nets
  • learning (artificial intelligence)
  • feature selection
  • feature extraction
  • data visualisation
  • convolutional neural nets
  • cognition
  • brain
  • biomedical imaging


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