Next Generation Real Time Healthcare Analytics via Secure Kubernetes Enabled ML Cloud
Abstract
The rapid evolution of digital health technologies, including electronic health records (EHRs), wearable devices, Internet of Medical Things (IoMT), and telemedicine platforms, has generated unprecedented volumes of real-time healthcare data. Harnessing this data for predictive and prescriptive analytics requires scalable, intelligent, and secure cloud-native infrastructures. This paper proposes a Next Generation Real-Time Healthcare Analytics framework powered by a Secure Kubernetes-Enabled Machine Learning (ML) Cloud. The architecture integrates containerized ML workloads, Kubernetes orchestration, real-time streaming pipelines, zero-trust security mechanisms, and automated compliance governance to ensure scalability, resilience, and regulatory adherence. Kubernetes enables dynamic resource allocation, fault tolerance, and high availability, while ML models deliver predictive diagnostics, anomaly detection, and clinical decision support. Security is embedded through role-based access control, network segmentation, encryption protocols, DevSecOps pipelines, and continuous monitoring aligned with HIPAA and GDPR standards. AI-driven observability enhances system performance and threat detection. The proposed framework supports hybrid and multi-cloud deployments, enabling healthcare enterprises to process high-velocity data streams with minimal latency while safeguarding sensitive patient information. This research contributes a comprehensive and secure cloud-native blueprint for next-generation healthcare analytics transformation
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
14927-14935 |
Published |
October 13, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Prof.Usha M (%2025). Next Generation Real Time Healthcare Analytics via Secure Kubernetes Enabled ML Cloud. International Journal of Science, Research and Technology , Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14927-14935. https://doi.org/10.15662/IJSRAT.2025.0805002 |
References
2. Surisetty, L. S. (2024). Improving Disease Detection Accuracy with Al and Secure Data Exchange through API Gateways. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3), 10346-10354.
3. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
4. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12-24.
5. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.
6. Mogil, V. B. (2023). Implementing role-based access control for healthcare data using SharePoint. International Journal of Engineering & Extended Technologies Research, 5(2), 6323–6333.
7. Kusumba, S. (2025). Integrated Order and Invoice Tracking: Optimizing Supply Chain Visibility And Financial Operations. Journal of International Crisis & Risk Communication Research (JICRCR), 8.
8. Ananth, S., & Saranya, A. (2016, January). Reliability enhancement for cloud services-a survey. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-7). IEEE.
9. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
10. Christadoss, J., Devi, C., & Mohammed, A. S. (2024). Event-Driven Test-Environment Provisioning with Kubernetes Operators and Argo CD. American Journal of Data Science and Artificial Intelligence Innovations, 4, 229-263.
11. Madheswaran, M., Dhanalakshmi, R., Ramasubramanian, G., Aghalya, S., Raju, S., & Thirumaraiselvan, P. (2024, April). Advancements in immunization management for personalized vaccine scheduling with IoT and machine learning. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 1566-1570). IEEE.
12. Gurajapu, A., & Garimella, V. (2025). Agile governance and cognitive automation in cloud security operations. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(3), 12133–12136.
13. Raj, A. M. A., Rajendran, S., & Vimal, G. S. A. G. (2024). Enhanced convolutional neural network enabled optimized diagnostic model for COVID-19 detection. Bulletin of Electrical Engineering and Informatics, 13(3), 1935-1942.
14. Genne, S. (2022). A secure architecture for real-time data exchange in HIPAA-compliant patient portals. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6202–6215.
15. Mudunuri, P. R. (2023). Automation-driven reliability engineering for public-sector biomedical systems. International Journal of Humanities and Information Technology (IJHIT), 5(1), 68–86.
16. Ponugoti, M. (2022). Integrating full-stack development with regulatory compliance in enterprise systems architecture. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(2), 6550–6563.
17. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
18. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
19. Sugumar, R. (2025). Separating Technology and Trust: A Survey Analysis of Patients’ Attitudes toward AI-Assisted Healthcare Decision-Making. International Journal of Humanities and Information Technology, 7(01), 72-79.
20. Natta, P. K. (2024). Designing trustworthy AI systems for mission-critical enterprise operations. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13828–13838. https://doi.org/10.15662/IJFIST.2024.0706003
21. Kasireddy, J. R. (2025). The cloud cost-optimization flywheel: A systematic approach to reducing infrastructure waste without compromising delivery velocity. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(2), 16075–16087
22. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
23. Gangina, P. (2022). Resilience engineering principles for distributed cloud-native applications under chaos. International Journal of Computer Technology and Electronics Communication, 5(5), 5760–5770.
24. Ananth, S., Radha, K., & Raju, S. (2024). Animal Detection In Farms Using OpenCV In Deep Learning. Advances in Science and Technology Research Journal, 18(1), 1.
25. Kondisetty, K., Panda, M. R., & Murthy, C. J. (2023). Customer Experience Enhancement in Omnichannel Banking Using Reinforcement Learning. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 565-600.
26. Ramidi, M. (2022). Building secure biometric systems for digital identity verification in aviation mobile apps. International Journal of Engineering & Extended Technologies Research, 4(4), 5036–5047.
27. Poornima, G., & Anand, L. (2024, April). Effective strategies and techniques used for pulmonary carcinoma survival analysis. In 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST) (pp. 1-6). IEEE.
28. Sriramoju, S. (2024). Optimizing data flow: A unified approach for product, pricing, and revenue sync in enterprise systems. International Journal of Engineering & Extended Technologies Research, 6(1), 7492–7503
29. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
30. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.