  Large-scale population-based studies in medicine are a key resource towards
better diagnosis, monitoring, and treatment of diseases. They also serve as
enablers of clinical decision support systems, in particular Computer Aided
Diagnosis (CADx) using machine learning (ML). Numerous ML approaches for CADx
have been proposed in literature. However, these approaches assume full data
availability, which is not always feasible in clinical data. To account for
missing data, incomplete data samples are either removed or imputed, which
could lead to data bias and may negatively affect classification performance.
As a solution, we propose an end-to-end learning of imputation and disease
prediction of incomplete medical datasets via Multigraph Geometric Matrix
Completion (MGMC). MGMC uses multiple recurrent graph convolutional networks,
where each graph represents an independent population model based on a key
clinical meta-feature like age, sex, or cognitive function. Graph signal
aggregation from local patient neighborhoods, combined with multigraph signal
fusion via self-attention, has a regularizing effect on both matrix
reconstruction and classification performance. Our proposed approach is able to
impute class relevant features as well as perform accurate classification on
two publicly available medical datasets. We empirically show the superiority of
our proposed approach in terms of classification and imputation performance
when compared with state-of-the-art approaches. MGMC enables disease prediction
in multimodal and incomplete medical datasets. These findings could serve as
baseline for future CADx approaches which utilize incomplete datasets.
