Dynamic Spatial-Temporal Graph Model for Disease Prediction

Advances in the field of Neural Networks, especially Graph Neural Networks (GNNs) has helped in many fields, mainly in the areas of Chemistry and Biology where recognizing and utilising hidden patterns is of much importance. In Graph Neural Networks, the input graph structures are exploited by using the dependencies formed by the nodes. The data can also be transformed in the form of graphs which can then be used in such models. In this paper, a method is proposed to make appropriate transformations and then to use the structure to predict diseases. Current models in disease prediction do not fully use the temporal features that are associated with diseases, such as the order of the occurrence of symptoms and their significance. In the proposed work, the presented model takes into account the temporal features of a disease and represents it in terms of a graph to fully utilize the power of Graph Neural Networks and Spatial-Temporal models which take into consideration of the underlying structure that change over time. The model can be efficiently used to predict the most likely disease given a set of symptoms as input. The model exhibits the best algorithm based on its accuracy. The accuracy of the algorithm is determined by the performance on the given dataset. The proposed model is compared with the existing baseline models and proves to be outstanding and more promising in the disease prediction.