Graph Convolutional Networks For Disease Mapping and Classification in Healthcare

In the context of healthcare, this study investigates the use of Graph A convolutional Networks (GCNs) for disease mapping along with classification. Based on an interpretivist philosophical thought, a descriptive design alongside secondary data collection is used in a deductive manner. The research creates a strong framework for sickness mapping, assesses how well GCNs adapt to varied health information, and compares their effectiveness with more conventional machine learning techniques in order to determine how suitable they are. An investigation is conducted into the understanding of GCN-based diagnosis models, offering valuable perspectives into their decision-making procedures. The findings support improved diagnostic precision, wellinformed treatment planning, along with precision in medical treatments. The emphasis when applying research results to medical procedures is on connection into systems that provide decision support, and ongoing improvement. The importance of model interpretability, the ability to be general as well realworld integration is highlighted by a critical analysis. Developing interpretability strategies and addressing ethical issues are among the recommendations. In order to ensure responsible deployment, future work ought to concentrate on improving GCN architectures, integrating multi-modal information and advocating interdisciplinary collaboration.