Prediction of heart disease using graph neural networks

Abstract

Cardiovascular Diseases (CVDs) have become increasingly crucial in recent years and have been regarded as the leading cause of death worldwide. Although it is necessary to detect and treat CVDs in their early stages, only 67% of heart diseases could be predicted by medical professionals. Motivated by recent advances in Graph Neural Networks (GNNs) that have ramified in a variety of industries, in this paper, we utilize three novel GNN models, including Graph Convolutional Network (GCN), GraphSAGE, and Graph Attention Network (GAT), to conduct the heart disease prediction task, comprised of two stages: table-to-graph transformation and Graph Neural Networks prediction. Experimental results show significant improvements with the utilization of GNNs compared with three novel Machine Learning models: Logistic Regression (LR), Naïve Bayes (NB), and Multi-Layer Perceptron (MLP), and GAT performs optimally among the three GNN models. To the best of our knowledge, this is the first work that predicts heart disease using GNNs.