Fusion: Practice and Applications FPA 2692-4048 2770-0070 10.54216/FPA https://www.americaspg.com/journals/show/3565 2018 2018 Research Scholar, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India R. R. Assistant Professor, Department of Information Technology, Annamalai University, Chidambaram, Tamilnadu, India K. Santhosh Kumar The emerging field of healthcare has taken severe measures to safeguard sensitive patient health-related information especially the information taken from the predictive model. In this study, a novel blockchain-based solution is proposed in correlation with the Fuzzy-enhanced CLSTM model (FCLSTM) for storing and transmitting the data securely for heart disease prediction systems by ensuring data integrity, confidentiality, and access control. The proposed model uses a blockchain-based network which is implemented to prevent the tampering or unauthorized access to patients’ health-related data. The process begins with techniques that incorporate the predicted heart disease information from the patient’s data and is encrypted by using the hashing algorithm. A secure hybrid blockchain-based data management framework (SHB-DMF) is designed for exchanging the patient’s health data which enhances scalability and accessibility to the healthcare environment. The system incorporates a SHAES-256 hybrid model for enhancing the data confidentiality and integrity before transmitting to the neural network (FCLSTM). The proposed model uses a smart contract for regulating data access by ensuring the entry of the authorized entities by providing a suitable decrypting mechanism and interacting with the patient’s data. The smart contracts can automate the data retrieval workflows by integrating the blockchain seamlessly with the prediction model. The security process is a three-phase process that includes defining the nodes, selecting of consensus mechanism, and establishing the governance structure for facilitating secure operations. The security and load testing ensure resilience to potential cyber threats and the scalability required for handling high transaction volumes of medical data. Deploying the proposed system provides a robust infrastructure that is tamper-resistant thus advancing the reliability of the cardiovascular prediction system. 2025 2025 262 275 10.54216/FPA.180219 https://www.americaspg.com/articleinfo/3/show/3565