Blockchain-Integrated IoT Framework for Patient Monitoring Using Optimized Multi-Scale Adaptive Graph Neural Networks

Abstract The increasing use of Internet of Things (IoT) devices in healthcare has significantly improved real-time patient monitoring. However, it also introduces critical challenges in data security, patient privacy, and the integration of heterogeneous data streams. To address these issues, we propose the first framework integrating Hyperledger Fabric with a Multi-Scale Adaptive Graph Neural Network (GNN) for real-time IoT healthcare systems, offering unmatched security and predictive accuracy. The blockchain layer ensures tamper-proof storage, enforces fine-grained access control, and builds trust among stakeholders, while the adaptive GNN captures temporal dynamics and complex interdependencies in physiological signals to detect anomalies. Experimental results on synthetic healthcare datasets show that our framework achieves a 99.6% accuracy, an AUC and F1-score of 1.0, and a blockchain throughput of 97.13 transactions per second, significantly outperforming traditional machine learning models by 10–15% in accuracy and delivering 2× higher throughput compared to prior blockchain-based systems. While current validation is based on synthetic data, real-world deployment is planned to confirm generalizability. This integrated approach paves the way for scalable, trustworthy, and equitable digital healthcare solutions, especially in resource-constrained and decentralized settings.