A graph neural network-based multi-context mining framework predicts emerging health risks to improve personalized healthcare

An emerging health risk prediction framework which uses Graph Neural Networks (GNN) as Multi-Context Mining mechanisms demonstrates high accuracy performance. The proposed system obtains different kinds of datasets from chronic disease information to behavioural patterns and mental health records before performing preprocessing. Our model predicts multiple dependent variables through advanced multivariate regression analysis to yield precise regression models with detailed feature maps. The method establishes an initial graphical structure through patient nodes that cluster together according to shared health characteristics and edge connections based on correlation values. The analysed context from mining drives an iterative growth of the graph based on GNN model implementation for latent risk detection. The framework uses patient relationships in the graph structure to foresee the development of comparable chronic conditions and related symptoms among patients. The framework integrates an adaptive clustering system alongside a dynamic graph expansion method which tracks time-dependent medical relationships between patients while creating optimized patient clusters. The implemented framework establishes a 92.4% accuracy level through performance assessments that evaluate precision levels of the regression model and clustering efficiency and overall robust framework performance. The model we developed shows successful capacity to recognize threatening health patterns while producing individualized predictive information. Through its significant developments in healthcare analytics this work enables proactive diagnosis alongside better treatment recommendations that produce better patient results.