Performance Optimization in Blockchain Networks for Healthcare Systems Using Deep Learning

Blockchain technology offers promising solutions for secure and efficient healthcare data management. However, scalability remains a significant challenge, particularly with the increasing volume of healthcare data. This paper proposes an advanced blockchain architecture that leverages deep learning techniques, specifically Long Short-Term Memory (LSTM) networks, for workload prediction and dynamic sharding to improve scalability and performance in healthcare systems. By dynamically adjusting shard configurations based on predicted workloads, the proposed system significantly improves transaction throughput and reduces latency while ensuring patient data privacy and integrity. Experimental results demonstrate a 56% increase in transaction throughput and a 16% reduction in latency compared to traditional sharding methods. Additionally, the integration of a Byzantine Fault Tolerance (BFT) consensus mechanism with the dynamic sharding approach ensures robust security and performance. The experimental results mark a significant step forward in scaling blockchain networks for healthcare, offering a comprehensive framework that balances efficiency, security, and privacy.