  Disease prediction is a well-known classification problem in medical
applications. GCNs provide a powerful tool for analyzing the patients' features
relative to each other. This can be achieved by modeling the problem as a graph
node classification task, where each node is a patient. Due to the nature of
such medical datasets, class imbalance is a prevalent issue in the field of
disease prediction, where the distribution of classes is skewed. When the class
imbalance is present in the data, the existing graph-based classifiers tend to
be biased towards the major class(es) and neglect the samples in the minor
class(es). On the other hand, the correct diagnosis of the rare positive cases
among all the patients is vital in a healthcare system. In conventional
methods, such imbalance is tackled by assigning appropriate weights to classes
in the loss function which is still dependent on the relative values of
weights, sensitive to outliers, and in some cases biased towards the minor
class(es). In this paper, we propose a Re-weighted Adversarial Graph
Convolutional Network (RA-GCN) to prevent the graph-based classifier from
emphasizing the samples of any particular class. This is accomplished by
associating a graph-based neural network to each class, which is responsible
for weighting the class samples and changing the importance of each sample for
the classifier. Therefore, the classifier adjusts itself and determines the
boundary between classes with more attention to the important samples. The
parameters of the classifier and weighting networks are trained by an
adversarial approach. We show experiments on synthetic and three publicly
available medical datasets. RA-GCN demonstrates the superiority compared to
recent methods in identifying the patient's status on all three datasets. The
detailed analysis is provided as quantitative and qualitative experiments on
synthetic datasets.
