A Scalable and Secure Data Analytics Framework for Decentralized Autonomous Healthcare Systems using Fuzzy Logic, Blockchain Sharding, Dynamic Network Slicing, and ECC

Healthcare systems are increasingly challenged by the complexity of managing scalable and secure data. Addressing this, the proposed novel framework integrates elliptic curve cryptography (ECC), dynamic network slicing, blockchain sharding, and fuzzy logic to enable efficient, adaptive, and secure data analytics. The framework processes healthcare data from IoT devices, wearable sensors, and patient records, ensuring real-time analytics, scalability, and robust security. By utilizing blockchain sharding for scalability, ECC for secure encryption, and fuzzy logic for decision-making, the architecture effectively overcomes the constraints of traditional systems. The results demonstrate significant improvements, including enhanced security (98%), reduced latency (10 ms), and higher scalability (5000 TPS). These advancements establish a reliable, decentralized foundation for predictive healthcare insights, resource optimization, and adaptive governance, setting a benchmark for modern healthcare data management systems.