Temporal Graph Neural Network and LSTM-based Hybrid Model for Early Cancer Prediction in Connected Healthcare Systems

Abstract

Cancer continues to be a significant global health concern, inflicting considerable financial and healthcare burdens on individuals, healthcare providers and national economies. Traditional diagnostic methods frequently do not identify tumours at an early and manageable stage, resulting in heightened treatment complexity and healthcare expenses. In this context, Early Cancer Prediction within Connected Healthcare Systems has become a significant research domain, utilising big data and predictive analytics to enhance healthcare outcomes. The suggested framework amalgamates diverse healthcare data sources, such as electronic health records, genomic databases, medical image repositories, wearable devices, and insurance claims, to construct an intelligent predictive model. Data preprocessing employs the StandardScaler technique for dataset normalisation, while recursive feature selection is utilised to discern the most pertinent features from the cancer prediction dataset, hence improving classification performance. A deep learning architecture based on LSTM is utilised for prediction, using a diffusion convolution kernel to capture delay propagation features, culminating in the DeepGraphLSTM model. Experimental findings indicate that the suggested model surpasses current mainstream methodologies for robustness and prediction accuracy, with an accuracy of 94.08%. The study comes to the conclusion that combining deep learning with big data analytics greatly improves early cancer detection and facilitates effective decision-making in networked healthcare settings.