Abstract

This research investigates the potential applications of Graph Neural Networks (GNNs) in cancer research and compares their performance to traditional machine learning techniques for predicting cancer outcomes. Using a systematic literature review methodology, and thematic textual analysis, GNNs and traditional machine learning algorithms are compared based on performance metrics such as accuracy, precision, recall, and F1 score. The research aims to provide insights of using GNNs for cancer research and their comparative performance in predicting cancer outcomes.