A Hybrid deep learning-based Decision-making framework for Scalability and Security in Blockchain-Powered Healthcare Systems

Abstract

Healthcare data generation is at an all-time high, with an increasing need for innovative solutions to tackle challenges related to scalability, security, and data privacy. Traditional centralized approaches that deal with the above problems, fail to provide secrecy and verifiability for data integrity, real-time data processing, and interoperability. In this paper, we introduce a modular Permissions based blockchain architecture integrated with hybrid Deep learning model to disrupt healthcare. In this context, blockchain offers decentralized and tamper-proof storage and access control via smart contracts, and hybrid deep learning exploits the strengths of Long Short Term Memory (LSTM) and Support Vector Machines (SVMs) for efficient classification and forecasting. Some advanced forms of encryption called homomorphic encryption allow computations to be done on encrypted data while keeping the data itself private. The framework uses IoT sensors to monitor health data in real time by measuring vital parameters such as heart rate, blood pressure, and glucose levels. By employing extensive experiments using Hyperledger Fabric, the proposed model outperforms in terms of latency, and has higher transaction throughput and better security against security attacks like Denial of Service (DoS), phishing, and collusion. The mathematical model established for encryption latency, network delay and energy efficiency confirms both the robust and efficient nature of the system. This framework provides a secure, scalable, and privacy-preserving healthcare solution, facilitating informed decision-making and better patient outcomes.