AI-Cloud Driven Distributed Healthcare Architecture Leveraging Blockchain and SVM for Data Privacy
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Abstract
The rapid digitization of healthcare services demands secure, scalable, and intelligent frameworks to manage sensitive patient data. This paper proposes an AI-Cloud Driven Distributed Healthcare Architecture that leverages Blockchain technology and Support Vector Machine (SVM) algorithms to ensure privacy, security, and efficient data management. The framework integrates AI-driven analytics for predictive healthcare insights, SVM for accurate classification and anomaly detection, and blockchain for immutable and decentralized data storage. Cloud computing provides scalable infrastructure, enabling real-time access, data sharing across distributed healthcare nodes, and high availability. By combining these technologies, the system ensures privacy-preserving patient data exchange, supports interoperable healthcare services, and enhances decision-making for clinicians and administrators. The architecture also addresses cybersecurity threats, minimizes unauthorized access, and ensures compliance with healthcare regulations. Simulation and case study results demonstrate the framework’s ability to deliver secure, intelligent, and resilient healthcare operations in distributed environments.
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1. Chen, Y., Paxson, V., & Katz, R. (2019). Understanding intrinsic vulnerabilities in cloud computing infrastructures. In Proceedings of the 2019 IEEE Symposium on Security and Privacy.
2. Lanka, S. (2024). Redefining Digital Banking: ANZ’s Pioneering Expansion into Multi-Wallet Ecosystems. International Journal of Technology, Management and Humanities, 10(01), 33-41.
3. Du, M., Li, F., Zheng, G., & Srikumar, V. (2018). DeepLog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.
4. Peddamukkula, P. K. (2024). Artificial Intelligence in Life Expectancy Prediction: A Paradigm Shift for Annuity Pricing and Risk Management. International Journal of Computer Technology and Electronics Communication, 7(5), 9447-9459.
5. Arjunan, T., Arjunan, G., & Kumar, N. J. (2025, May). Optimizing Quantum Support Vector Machine (QSVM) Circuits Using Hybrid Quantum Natural Gradient Descent (QNGD) and Whale Optimization Algorithm (WOA). In 2025 6th International Conference for Emerging Technology (INCET) (pp. 1-7). IEEE
6. Fiore, U., De Santis, A., Perla, F., Zanetti, P., & Palmieri, F. (2019). Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Information Sciences, 479, 448 455.
7. Nallamothu, T. K. (2025). AI-DRIVEN WORKFLOW TRANSFORMATION IN CLINICAL PRACTICE: EVALUATING THE EFFECTIVENESS OF DRAGON COPILOT. International Journal of Research and Applied Innovations, 8(3), 12298-13013.
8. He, K., Zhang, X., Ren, S., & Sun, J. (2020). Misconfiguration detection in container orchestration configurations using language models. Journal of Cloud Computing: Advances, Systems and Applications, 9(1), 25 38.
9. Azmi, S. K. (2022). Computational Knot Theory for Deadlock-Free Process Scheduling in Distributed IT Systems. Well Testing Journal, 31(1), 224-239.
10. Sabillon, R., Cavaller, V., & Laresgoiti, I. (2018). Data governance and policy enforcement in financial institutions: challenges and methods. Journal of Financial Regulation and Compliance, 26(4), 541 558.
11. Sangannagari, S. R. (2025). ARCHITECTURE FOR A BLOCKCHAIN-BASED CERTIFICATION PLATFORM FOR EXPLOSION-PROOF DEVICES. International Journal of Research and Applied Innovations, 8(2), 11956-11975.
12. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2020). Artificial intelligence using TOPSIS method. Journal of Computer Science Applications and Information Technology, 5(1), 1–7. https://doi.org/10.15226/2474-9257/5/1/00147
13. Prabaharan, G., Sankar, S. U., Anusuya, V., Deepthi, K. J., Lotus, R., & Sugumar, R. (2025). Optimized disease prediction in healthcare systems using HDBN and CAEN framework. MethodsX, 103338.
14. Win, K. T., Tun, Z., & Thein, M. M. (2021). Extracting privacy requirements from regulatory texts using NLP. Journal of Information Security and Applications, 58, Article 102690.
15. Thambireddy, S., Bussu, V. R. R., & Pasumarthi, A. (2025). Leveraging Sap Joule AI for Autonomous Business Process Optimization In 2025. Journal of Artificial Intelligence General Science (JAIGS) ISSN:3006-4023, 8(1), 241–257. https://doi.org/10.60087/jaigs.v8i1.382
16. Xu, J., Yousuf, M., & Li, J. (2020). Framework for integrated anomaly detection and policy enforcement in cloud infrastructures. Proceedings of the IEEE International Conference on Cloud Engineering (IC2E).
17. Konda, S. K. (2023). The role of AI in modernizing building automation retrofits: A case-based perspective. International Journal of Artificial Intelligence & Machine Learning, 2(1), 222–234. https://doi.org/10.34218/IJAIML_02_01_020
18. Gandhi, S. T. (2024). Enhancing Software Security with AI-Powered SDKs: A Framework for Proactive Threat Mitigation. International Journal of Computer Technology and Electronics Communication, 7(2), 8507-8514.
19. Reddy, B. V. S., & Sugumar, R. (2025, April). Improving dice-coefficient during COVID 19 lesion extraction in lung CT slice with watershed segmentation compared to active contour. In AIP Conference Proceedings (Vol. 3270, No. 1, p. 020094). AIP Publishing LLC.
20. Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A Deep Learning Approach for Intrusion Detection using Recurrent Neural Networks. IEEE Access, 7, 8412 8421.
21. Dave, B. L. (2023). Enhancing Vendor Collaboration via an Online Automated Application Platform. International Journal of Humanities and Information Technology, 5(02), 44-52.
22. Zhang, Y., & Jiang, H. (2019). Log anomaly detection with sequence models and log parsing. Proceedings of the 2019 ACM Symposium on Applied Computing.