Disease prediction is a well-known classification problem in medical applications. Graph neural networks provide a powerful tool for analyzing the patients' features relative to each other. Recently, Graph Convolutional Networks (GCNs) have particularly been studied in the field of disease prediction. Due to the nature of such medical datasets, the class imbalance is a familiar issue in the field of disease prediction. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es). Meanwhile, the correct diagnosis of the rare true-positive cases among all the patients is vital. In conventional methods, such imbalance is tackled by assigning appropriate weights to classes in the loss function; however, this solution 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 Re-weighted Adversarial Graph Convolutional Network (RA-GCN) to enhance the performance of the graph-based classifier and prevent it from emphasizing the samples of any particular class. This is accomplished by automatically learning to weigh the samples of the classes. For this purpose, a graph-based network is associated with each class, which is responsible for weighing the class samples and informing the classifier about the importance of each sample. Therefore, the classifier adjusts itself and determines the boundary between classes with more attention to the important samples. The parameters of the classifier and weighing networks are trained by an adversarial approach. At the end of the adversarial training process, the boundary of the classifier is more accurate and unbiased. We show the superiority of RA-GCN on synthetic and three publicly available medical datasets compared to the recent method.