Graph Convolutional Networks for Disease Network Analysis in Healthcare

The use of Graph Convolutional Networks (GCNs) in disease network analysis in the healthcare industry has been pioneered by this research. The adoption of GCNs is prompted by the limitations of current methods in collecting complex relationships within large datasets. Various healthcare datasets are used to develop and assess a PyTorch-based GCN model that makes use of an interpretivism philosophy and a deductive approach. The model's superiority over conventional approaches is demonstrated by the results, which also provide fresh perspectives on disease relationships. The interpretative analysis uncovers subtle patterns that advance our comprehensive knowledge of the dynamics of disease. Subsequent research will focus on improving GCNs for real-time applications, scalability, as well as multi-modal integration of information. This work promises to have revolutionary effects on healthcare analytics by utilizing cutting-edge deep-learning techniques to decipher complex disease networks.