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Structural data from Electronic Health Records as complementary informationto imaging data for disease prediction. We incorporate novel weighting layerinto the Graph Convolutional Networks, which weights every element ofstructural data by exploring its relation to the underlying disease. Wedemonstrate the superiority of our developed technique in terms ofcomputational speed and obtained encouraging results where our methodoutperforms the state-of-the-art methods when applied to two publicly availabledatasets ABIDE and Chest X-ray in terms of relative performance for theaccuracy of prediction by 5.31 % and 8.15 % and for the area under the ROCcurve by 4.96 % and 10.36 % respectively. Additionally, the model islightweight, fast and easily trainable.

Multi Layered-Parallel Graph Convolutional Network (ML-PGCN) for Disease Prediction