Articles Open Access
Explainable Predictive Analytics for Healthcare Interoperability in Cloud-Native Smart Systems
Independent Researcher, Canada
Research Assistant, Nusantara Global Universit, Indonesia
Abstract
The vision for a cloud-native architecture capable of supporting data exchange and predictive analytics across deployed smart health systems relies on two accepted concepts: (i) interoperability, the ability of an information technology system or product to work with other information technology systems or products; and (ii) predictive analytics, technology that analyzes current and historical facts to make predictions about future events. The proposed architectural framework focuses on data governance, and in particular the aspects of provenance, privacy, and analysis methods. A privacy-preserving approach to data classification detects and replaces sensitive information elements such as social security numbers, addresses, or credit card numbers. The management of data exchange operations is aligned with the requirements of the health domain data model. Predictive analytics use Explainable Artificial Intelligence techniques to provide interpretable predictions for clinical users.
The healthcare domain is one of the sectors where the use of smart systems has been accelerated because of the requirements imposed by the COVID-19 pandemic. Many Cloud-Native Smart Systems are designed, implemented, and deployed on the Cloud to support different Health software applications. Such systems, although deployed in the Cloud, do not have the ability to interact with each other owing to a lack of interoperability. Large amounts of data are collected from different users and devices; the creation of predictive models is an important task to help detect patterns, trends, and predict future values in health, analyzing patients' data to decide the presence of a health problem. A Cloud-Native architecture able to perform predictive analytics in the health domain and that supports interoperability among data is needed.
Keywords
Cloud computingExplainable AIHealthcareInteroperabilityPredictive analyticsExplainable Artificial Intelligence (XAI)Predictive Analytics in HealthcareHealthcare InteroperabilityCloud-Native ArchitecturesSmart Healthcare SystemsClinical Decision Support Systems (CDSS)Machine Learning Model TransparencyHealth Data Integration and StandardsScalable Cloud Computing for HealthcareTrustworthy and Ethical AI in Medicine
DOI
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 10 |
| Number | 4 |
| Pages | 19294-19309 |
| Published | 2026-08-13 |
| Issue | Vol. 10 No. 4 (2026): International Research Journal of Innovative Engineering (IRJIE) |
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4. Moosavi, S., Farajzadeh-Zanjani, M., Razavi-Far, R., Palade, V., & Saif, M. (2024). Explainable AI in manufacturing and industrial cyber-physical systems: A survey. Electronics, 13(17), 3497.
5. Inala, R. (2026). Cloud-Native AI and MDM Framework for Next-Generation Insurance and Retirement Data Products. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(3), 5050-5063.
6. Nagabhyru, K. C., Gadi, A. L., Seenu, A., Davuluri, P. N., Segireddy, A. R., & Pamisetty, V. (2026, March). Towards Automated Financial Risk Scoring in Automotive Financing with Explainable Machine Learning. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1-6). IEEE.
7. Davuluri, P. N., Segireddy, A. R., & Sheelam, G. K. (2026). Comment on" Machine learning-based prediction of CAC-defined cardiovascular risk using routine health examination data: a retrospective cross-sectional study in a Taiwanese population". Journal of the Formosan Medical Association= Taiwan yi zhi, S0929-6646.
8. Vankayalapati, R. K., Polineni, T. N. S., Ahammad, S. H., Pandugula, C., & Selvan, R. S. (2026). IoT-Enabled Augmented Reality for Real-Time Equipment Diagnosis. In Virtual Reality, Real Emergency (pp. 104-121). CRC Press.
9. Kannan, S. (2026). Generative AI for Smart Agricultural Equipment Automation and Pharmaceutical Crop Yield Prediction. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(3), 1068-1074.
10. Kritzinger, W., Karner, M., Traar, G., Henjes, J., & Sihn, W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), 1016–1022.
11. Krishnan, M., Nandan, B. P., Rongali, S. K., Meda, R., Kalisetty, S., & Singireddy, J. (2026, June). AI-Driven Data Engineering and Predictive Analytics Framework for Semiconductor Supply Chain Optimization and Digital Infrastructure Modernization. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
12. Pandiri, L. (2026). AI-Powered Predictive Analytics for Climate-Aware Flood and Mobile Home Insurance Risk Management. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 9(3), 834-847.
13. Karabulut, E., Pileggi, S. F., Groth, P., & Degeler, V. (2023). Ontologies in digital twins: A systematic literature review. Journal of Industrial Information Integration.
14. Inala, R., Sheelam, G. K., Aitha, A. R., Lakshmi, A. U., Nagabhyru, K. C., & Segireddy, A. R. (2026, March). Architecting Hybrid Data Products Using AI/ML and Agentic AI for Group Insurance and Retirement Solution Platforms with Advanced Data Governance. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1-6). IEEE.
15. Kummari, D. N., Aitha, A. R., Kumar, M. V. K., Pandiri, L., Gottimukkala, V. R. R., & Nagubandi, A. R. (2026, March). Real-Time AI-Driven Audit and Compliance Framework for Smart Manufacturing Financial Workflow. In 2026 IEEE International Conference on AI Engineering and Innovations (AIEI) (pp. 1-5). IEEE.
16. Nandan, B. P. (2026). Cloud-Scale Data Engineering for Real-Time Semiconductor Testing and AI-Powered Chip Diagnostics. International Journal of Computer Technology and Electronics Communication, 9(3), 1058-1070.
17. Sanku, R., Recharla, M., BG, M. B., & Chikop, S. A. (2026, February). Privacy-Preserving Federated Learning Framework with Adaptive Aggregated Gradient Perturbation for IoT Healthcare Systems. In 2026 3rd International Conference on Integrated Intelligence and Communication Systems (ICIICS) (pp. 1-6). IEEE.
18. Vankayalapati, R. K., Polineni, T. N. S., Ahammad, S. H., Pandugula, C., & Selvan, R. S. (2026). IoT-Enabled Augmented Reality for Real-Time Equipment Diagnosis. In Virtual Reality, Real Emergency (pp. 104-121). CRC Press.
19. Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: Enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971.
20. Hoffmann, R., & Reich, C. (2023). A systematic literature review on artificial intelligence and explainable artificial intelligence for visual quality assurance in manufacturing. Electronics, 12(22), 4572.
21. Bitencourt, J., Wooley, A., & Harris, G. (2025). Verification and validation of digital twins: A systematic literature review for manufacturing applications. International Journal of Production Research, 63(1), 342–370.
22. Kolla, S. H., Nagabhyru, K. C., & Kummari, D. N. (2026). Letter to the Editor re:“CurvAssist: An AI assisted pipeline for penile shaft segmentation/curvature measurement in children with hypospadias”. Journal of Pediatric Urology
23. Lattanzi, L., & Rossi, M. (2021). Digital twin for smart manufacturing: A review of concepts towards a practical industrial implementation. International Journal of Computer Integrated Manufacturing, 34(6), 567–597.
24. Genga, L., & Winter, K. (2025). Artificial intelligence in conformance checking: State of the art and research agenda. Process Science, 2, Article 9.
25. Liu, S., Zheng, P., & Bao, J. (2024). Digital twin-based manufacturing system: A survey based on a novel reference model. Journal of Intelligent Manufacturing, 35(6), 2517–2546
26. Garapati, R. S., Aitha, A. R., & Singireddy, S. (2026). Secure Cloud Architecture for Privacy-Preserving Machine Learning on Electronic Health Records with Web-Based Analytics Tools. In International Conference on Intelligent Human Computer Interaction (pp. 407-417). Springer, Cham.
27. Onaji, I., Tiwari, D., Soulatiantork, P., Song, B., & Tiwari, A. (2022). Digital twin in manufacturing: Conceptual framework and case studies. International Journal of Computer Integrated Manufacturing, 35(8), 831–858.
28. Peruthambi, V., Singireddy, S., Kummari, D. N., Nandan, B. P., Pamisetty, V., & Amistapuram, K. (2026, June). Adaptive Security Framework for AI-Driven Risk Assessment and Compliance Automation in Cloud Security and Vulnerability Management within the Insurance Domain. In 2026 6th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
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