RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data

TL;DR

This paper introduces RA-GCN, a novel graph convolutional network that addresses class imbalance in disease prediction by using adversarial training and class-specific weighting networks, improving accuracy on medical datasets.

Contribution

The paper proposes a Re-weighted Adversarial GCN that effectively handles class imbalance by integrating class-specific weighting networks trained adversarially, enhancing disease prediction accuracy.

Findings

RA-GCN outperforms recent methods on three medical datasets.

The approach effectively balances class importance in imbalanced datasets.

Quantitative and qualitative analyses confirm the method's superiority.

Abstract

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.