  Structural data from Electronic Health Records as complementary information
to imaging data for disease prediction. We incorporate novel weighting layer
into the Graph Convolutional Networks, which weights every element of
structural data by exploring its relation to the underlying disease. We
demonstrate the superiority of our developed technique in terms of
computational speed and obtained encouraging results where our method
outperforms the state-of-the-art methods when applied to two publicly available
datasets ABIDE and Chest X-ray in terms of relative performance for the
accuracy of prediction by 5.31 % and 8.15 % and for the area under the ROC
curve by 4.96 % and 10.36 % respectively. Additionally, the model is
lightweight, fast and easily trainable.
