aspg-3-2559-1791417768 20261008000248 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 15 1 Ahmad Raza Khan Department of Information Technology, College of Computer and Information Sciences, Majmaah University, AlMajmaah, 11952, Saudi Arabia Abdul Khader Jilani Department of Computer Science, University of Technology Bahrain, Bahrain The increasing adoption of cloud computing in healthcare presents immense opportunities for disease prediction, while raising critical privacy concerns. This study proposes a novel privacy-preserving scheme that leverages advanced cryptographic techniques, blockchain technology and deep learning approach within a cloud platform, to ensure secure data handling and accurate disease prediction. The proposed methodology encompasses authentication, encryption, blockchain-based transmission, and a deep learning-based heart disease prediction system (HDPS). Through rigorous authentication protocols and two-level security mechanisms, patient data is securely encrypted using RSA and Blowfish encryption before storage in the cloud. Blockchain technology facilitates secure data transmission, ensuring integrity and traceability. At the receiver end, data decryption precedes input into the HDPS, comprising artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN). The HDPS incorporates data preprocessing, feature extraction, feature selection, and a deep learning-based prediction model, achieving remarkable accuracy (0.9941) in heart disease prediction. Implemented in MATLAB, this approach offers a robust framework for privacy-preserving heart disease prediction in cloud-based healthcare systems. 2024 98 119 2559 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.150109 https://www.americaspg.com/journal/3/article/2559 https://www.americaspg.com/storage/01709671098.pdf