Cognitive Healthcare Cloud Framework Using AI for Predictive Patient Risk Modeling and Clinical Decision Intelligence
DOI:
https://doi.org/10.15680/IJCTECE.2026.0902018Keywords:
Cognitive healthcare cloud, artificial intelligence, predictive risk modeling, clinical decision intelligence, machine learning, cloud computing, healthcare analytics, patient care, decision support systems, digital healthAbstract
The advancement of cloud computing and artificial intelligence (AI) has enabled the development of cognitive healthcare systems that enhance predictive analytics and clinical decision-making. This study proposes a cognitive healthcare cloud framework that integrates AI-driven predictive patient risk modeling with intelligent clinical decision support. Traditional healthcare systems often struggle with fragmented data, delayed decision-making, and limited predictive capabilities. The proposed framework addresses these challenges by leveraging machine learning and deep learning models within a scalable cloud infrastructure.The system focuses on predictive patient risk modeling to identify potential health issues such as disease progression, hospital readmissions, and critical events. By analyzing large volumes of structured and unstructured healthcare data, the framework enables proactive interventions and personalized treatment strategies. Additionally, the integration of clinical decision intelligence supports healthcare professionals with real-time insights and evidence-based recommendations.
The research evaluates the framework through conceptual analysis and simulated scenarios. Findings indicate that the cognitive healthcare cloud significantly improves predictive accuracy, operational efficiency, and patient outcomes. However, challenges such as data privacy, interoperability, and system integration must be addressed. This study contributes to the development of intelligent healthcare systems that support proactive, data-driven, and patient-centric care delivery.
References
1. Gurram, S. (2025). Training Data Provenance and IP Compliance at Enterprise Scale. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 405-416.
2. Agarwal, S. (2022). Observability in Microservices: From Traditional Monitoring to Distributed System Intelligence. International Journal of Computer Technology and Electronics Communication, 5(6), 16220-16226.
3. Mogili, V. B. (2026). Designing Trustworthy AI in Healthcare: Experiences with Copilot Agents, Agentic Models, and RAG Integration. IPHO-Journal of Advance Research in Science And Engineering, 4(2), 19-25.
4. Kale, A., & Viswanathan, S. (2025). Global Surge in Banking Frauds: An International Management Perspective. International Journal of Accounting and Management Sciences, 4(4).
5. Anbazhagan, K. (2025). Next-Generation Enterprise Cloud AI for Healthcare: Secure CNN Pipelines and Privacy Controls. International Journal of Future Innovative Science and Technology (IJFIST), 8(6), 15980.
6. Katta, T. B. (2025, April). AI-Enhanced Orchestration in Hybrid Cloud Enterprise Integration: Transforming Enterprise Data Flows. In International Conference of Global Innovations and Solutions (pp. 118-129). Cham: Springer Nature Switzerland.
7. Hossen, M. S., Rahman, M. R., Alim, M. A., Tohfa, N. A., Rahman, M., & Rasul, I. (2026). Parallel Deep Learning for Cybersecurity-Oriented Multi-Platform Social Media Bot Detection. Frontiers in Computer Science and Artificial Intelligence, 5(2), 13-20.
8. Padala, S. (2025). Predictive AI in Healthcare Contact Centers: A Multi-Layered Approach to Patient Care Optimization. Journal Of Multidisciplinary, 5(7), 335-341.
9. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87-94.
10. Kanthakhoo, N. (2023). Liquid Biopsy–Based Biomarkers for Early Detection of Breast and Colorectal Cancer. SRMS JOURNAL OF MEDICAL SCIENCE, 8(02), 152-160.
11. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
12. Grandhe, K. (2025). Leveraging SAP S/4HANA and embedded analytics for real-time financial reporting. International Journal of Multidisciplinary Research and Growth Evaluation, 6(4), 1446–1448. https://doi.org/10.54660/.IJMRGE.2025.6.4.1446-1448
13. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
14. Sravanthi Mallireddy, D. R. S. (2024). Howzs Digital Transformation Impacted on HealthCare and Financial Services. Journal of Technological Innovations, 5(3).
15. Sarabhu, V. B., & Balaji, V. (2018). Advanced memory virtualization technique for efficient access of data resources in cloud environment. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 1(3), 623–629.
16. Mangukiya, M. (2025). Advanced testing and validation frameworks for high-reliability multi-board electronic systems. International Journal of Computational and Experimental Science and Engineering, 11(4).
17. Subramani, V. (2024). Dynamic scaling in e-commerce platforms: Microservices for latency, compliance, and resilience. Computer Fraud and Security, 2024(11). https://computerfraudsecurity.com/index.php/journal/article/view/879
18. Chaturvedi, V. (2025). Disease Diagnostic Systems based on AI-Applications in Healthcare: Models, Challenges, and Future Directions. International Journal of Emerging Research in Engineering and Technology, 6(4), 207-217.
19. Murugeshwari, B., Amirthavalli, R., Sri, C. B., & Pari, S. N. (2023). Hybrid key authentication scheme for privacy over adhoc communication. arXiv preprint arXiv:2304.14652.
20. Kothokatta, L. (2023). AI-Augmented Quality Engineering for MLOps: Intelligent Test Orchestration and Model Reliability on AWS. International Journal of Computer Technology and Electronics Communication, 6(4), 7324-7330.
21. Niture, N., & Abdellatif, I. (2025). A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine learning. Multimedia Tools and Applications, 84(18), 19009-19037.
22. Hasib, A., Akib, A. S. M., Ankur, N. D., & Giri, A. (2026). Dual-Modality IoT Framework for Integrated Access Control and Environmental Safety Monitoring with Real-Time Cloud Analytics. arXiv preprint arXiv:2601.20366.
23. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
24. Dama H. B. (2025). Automated database provisioning in CI/CD pipelines using Ansible and Azure DevOps. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(3), 9974–9981.
25. Gopinathan, V. R. (2024). Cyber-Resilient Digital Banking Analytics Using AI-Driven Federated Machine Learning on AWS. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8419-8426.
26. Ganesan, M. (2024). Transforming home electronics customer self-installation experience with AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 14319–14327.
27. Ranjith Rajasekharan. (2018). Infrastructure as code: Transforming enterprise IT operations. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 1(1), 8–15.
28. Nair, S. G. (2025). Designing Secure and Scalable Microservices for Threat Detection: Engineering Patterns from Endpoint Security Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11200-11209.
29. Kothokatta, L. (2025). Parallel Automation for Cross-Browser and Cross-Device Validation in OTT Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(5), 12919-12928.
30. 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.
31. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
32. Dhinakaran, D., Prathap, P. J., Selvaraj, D., Kumar, D. A., & Murugeshwari, B. (2022). Mining privacy-preserving association rules based on parallel processing in cloud computing. International Journal of Engineering Trends and Technology, 70(3), 284-294.
33. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.
34. Viswanathan, V. (2025). Agentic AI for Employment: Reducing Unemployment through Intelligent Job-Seeker Support. LEX LOCALIS–Journal of Local Self-Government.
35. Sravanthi Mallireddy, D. R. S. (2024). Howzs Digital Transformation Impacted on HealthCare and Financial Services. Journal of Technological Innovations, 5(3).
36. Akila, R. (2024). A deep reinforcement learning approach for optimizing inventory management in the agri-food supply chain. J. Electrical Systems, 20(4s), 2238-2247.
37. Meka, S. (2022). Engineering Insurance Portals of the Future: Modernizing Core Systems for Performance and Scalability. International Journal of Computer Science and Information Technology Research, 3(1), 180-198.
38. Nallamothu, T. K. (2024). Empowering Analysts with AI: Evaluating Nuance DAX Copilot in Business Intelligence Environments. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10624-10633.
39. Gentyala, R. (2024). From Bronze to Broken: A Grounded Theory Study of Anti-Patterns and Accruing Data Debt in Medallion Lakehouse Deployments. European Journal of Advances in Engineering and Technology, 11(1), 90–100.
40. Kanojiya, S., Yasin, M., Rahman, M. B., Ahmad, S., & Hasan, M. (2025). Business Analytics for Quality Improvement: A Case Study in Healthcare Systems. Nvpubhouse Library for International Journal of Medical Science and Public Health Research, 6(10), 137-162.
41. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
