Converging Technologies for Health Prediction and Intrusion Detection in Internet of Healthcare Things With Matrix- Valued Neural Coordinated Federated Intelligence

This paper introduces Matrix-Valued Neural Coordinated Federated Deep Extreme Machine Learning, a novel approach for enhancing health prediction and intrusion detection on the Internet of Healthcare Things (IoHT). By leveraging Machine Learning (ML), blockchain, and Intrusion Detection Systems (IDS), this technique ensures the security of medical data while enabling predictive health analytics. The IoHT, characterized by its vast network of interconnected devices, poses significant challenges in security and confidentiality. However, the integration of proposed technique empowers healthcare systems to proactively address these concerns while enhancing patient outcomes and reducing healthcare costs. Smart healthcare, enabled by ML and blockchain, is revolutionizing healthcare 5.0. Healthcare systems may employ IoHTs’ intelligent and interactive characteristics using proposed approach. Despite its benefits, medical data aggregation poses security, ownership, and regulatory compliance challenges. Federated Learning (FL) is a key technique for distributed learning that protects data. The proposed architecture has several unique benefits like 1) it provides a thorough examination of the incorporation of blockchain technology with FL for healthcare 5.0; 2) it takes the lead in creating a robust healthcare monitoring system that utilizes blockchain technology and IDS to identify and prevent harmful actions; 3) the development of crucial blockchain elements by means of mutual neuronal synchronization of Artificial Neural Networks (ANNs) showcases pioneering progress in safeguarding medical data; and 4) the framework underwent a thorough empirical assessment and outperformed existing methods in accurately predicting intrusion detection and disease prediction, achieving an impressive efficiency rate of 97.75% and 98% respectively. This development represents a major step forward in improving security and predictive abilities within the IoHT ecosystem, offering the potential for revolutionary advancements in healthcare delivery and patient care.