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Global Multidisciplinary Journal

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Architecting Cloud-Native, Observability-Driven Healthcare Platforms: Integrating DevOps, DataOps, and Machine Learning for Scalable Cardiovascular Prediction Systems

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Dr. Helena Sørensen 1 ,
4 Department of Digital Systems and Innovation University of Copenhagen, Denmark

Abstract

The accelerating convergence of cloud-native architectures, enterprise integration platforms, and machine learning-driven healthcare analytics has redefined the technological landscape of modern clinical systems. Cardiovascular diseases continue to represent a leading cause of global mortality, demanding predictive and scalable digital infrastructures capable of integrating clinical intelligence with enterprise-grade cloud environments. While prior scholarship has examined individual domains—such as scalable Heroku-Salesforce integrations, observability in cloud-native systems, cloud data service architectures, and supervised machine learning for heart disease prediction—a comprehensive synthesis bridging cloud-native engineering, enterprise integration, and intelligent healthcare analytics remains insufficiently explored.

This study develops a theoretically grounded and publication-ready framework for designing cloud-native, observability-driven healthcare platforms capable of supporting intelligent heart disease prediction systems at scale. Drawing strictly from the provided scholarly corpus, the research synthesizes insights from scalable application engineering, multitenant cloud data architectures, digital transformation theory, DevOps-DataOps-MLOps convergence, and intelligent cloud-based cardiovascular prediction models. The methodological approach employs conceptual architectural synthesis, mapping theoretical constructs from cloud transformation literature to healthcare machine learning deployment requirements.

The results propose a layered architecture integrating cloud-native transformation patterns, infrastructure observability, enterprise integration via iPaaS, multitenant data services, and supervised learning pipelines for cardiovascular risk prediction. Emphasis is placed on scalability, compliance, operational transparency, and digital maturity. The discussion examines governance challenges, compliance implications in healthcare, operational resilience, and the strategic role of deliberate digital transformation in sustaining cloud-native health ecosystems.

This research contributes an integrated theoretical model for designing scalable, compliant, and observability-enabled cardiovascular prediction platforms within modern enterprise cloud environments, offering both academic insight and architectural guidance for future intelligent healthcare systems.

 

Keywords

Cloud-native healthcare, Observability, MLOps integration, Cardiovascular prediction

References

📄 Khan, M. A. (2020). Intelligent cloud based heart disease prediction system empowered with supervised machine learning. Computers, Materials and Continua, 65(1), 139-151.
📄 Marie-Magdelaine, N. (2021). Observability and resources managements in cloud-native environments (Doctoral dissertation, Université de Bordeaux).
📄 Michael, S., & Sophia, M. (2021). The role of iPaaS in future enterprise integrations: Simplifying complex workflows with scalable solutions. International Journal of Trend in Scientific Research and Development, 5(6), 1999-2014.
📄 Narasayya, V., & Chaudhuri, S. (2021). Cloud data services: Workloads, architectures and multitenancy. Foundations and Trends® in Databases, 10(1), 1-107.
📄 Nayyar, A., Gadhavi, L., & Zaman, N. (2021). Machine learning in healthcare: Review, opportunities and challenges. In Machine Learning and the Internet of Medical Things in Healthcare (pp. 23-45). Academic Press.
📄 Pal, P. (2022). The adoption of waves of digital technology as antecedents of digital transformation by financial services institutions. Journal of Digital Banking, 7(1), 70-91.
📄 Parikh, K., & Johri, A. (2022). Combining DataOps, MLOps and DevOps: Outperform analytics and software development with expert practices on process optimization and automation. BPB Publications.
📄 Ravilla, H. (2025). Building Scalable Applications with Heroku and Salesforce Integration. American Journal of Technology, 4(3), 15–36. https://doi.org/10.58425/ajt.v4i3.454
📄 Reznik, P., Dobson, J., & Gienow, M. (2019). Cloud native transformation: Practical patterns for innovation. O’Reilly Media.
📄 Salunkhe, V., Pakanati, D., Cherukuri, H., Khan, S., & Jain, D. A. (2021). The impact of cloud native technologies on healthcare application scalability and compliance. SSRN.
📄 Sikeridis, D., Papapanagiotou, I., Rimal, B. P., & Devetsikiotis, M. (2017). A comparative taxonomy and survey of public cloud infrastructure vendors. arXiv preprint arXiv:1710.01476.
📄 1Tardieu, H., Daly, D., Esteban-Lauzán, J., Hall, J., & Miller, G. (2020). Deliberately digital. Springer International Publishing.
📄 Upadhyay, N. (2018). CABology: Value of cloud, analytics and big data trio wave. Springer.

How to Cite

Dr. Helena Sørensen. (2026). Architecting Cloud-Native, Observability-Driven Healthcare Platforms: Integrating DevOps, DataOps, and Machine Learning for Scalable Cardiovascular Prediction Systems. Global Multidisciplinary Journal, 5(01), 146-152. https://www.grpublishing.org/journals/index.php/gmj/article/view/337

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