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

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.

References in corpus (13)

• Scikit-learn: Machine Learning in Python
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Geometric deep learning: going beyond Euclidean data
• Spectral Networks and Locally Connected Networks on Graphs
• An overview of deep learning in medical imaging focusing on MRI
• Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images
• Class Imbalance Problem in Data Mining Review
• Revisiting Graph Neural Networks: All We Have is Low-Pass Filters
• SIGN: Scalable Inception Graph Neural Networks
• Influence of Resampling on Accuracy of Imbalanced Classification
• Directed Graph Convolutional Network
• Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
• Zoom in to where it matters: a hierarchical graph based model for mammogram analysis

Cited by in corpus (3)

• GATE: Graph CCA for Temporal SElf-supervised Learning for Label-efficient fMRI Analysis
• Topology-Imbalance Learning for Semi-Supervised Node Classification
• GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference