Cognitive Cloud Architectures for Smart Healthcare Systems
DOI:
https://doi.org/10.15662/IJEETR.2023.0505001Keywords:
Cognitive cloud computing, Smart healthcare systems, Artificial intelligence, Machine learning, Edge computingAbstract
The integration of cognitive computing with cloud architectures has ushered in a new era of smart healthcare systems capable of delivering personalized, efficient, and scalable medical services. Cognitive cloud architectures leverage artificial intelligence (AI), machine learning (ML), and big data analytics within cloud platforms to enhance decision-making, patient monitoring, and resource management. This paper presents an in-depth exploration of cognitive cloud architectures tailored for smart healthcare environments, addressing critical challenges such as data heterogeneity, real-time processing, security, and privacy compliance. We propose a modular, scalable architecture that combines edge computing with centralized cloud resources to enable low-latency analytics and adaptive healthcare services. The architecture supports continuous learning from diverse medical data sources, including electronic health records, wearable devices, and imaging systems, enabling predictive diagnostics and personalized treatment plans. Extensive simulations and prototype implementations demonstrate that the proposed architecture improves system responsiveness, enhances diagnostic accuracy by 20%, and reduces data transmission latency by 30% compared to traditional cloud-based healthcare models. Furthermore, the cognitive capabilities enable proactive anomaly detection and intelligent resource allocation, optimizing healthcare delivery in dynamic environments. We also discuss integration challenges, such as interoperability among heterogeneous healthcare systems and compliance with healthcare regulations like HIPAA and GDPR. The findings highlight the potential of cognitive cloud architectures to transform healthcare by fostering smarter, more adaptive, and patient-centric systems, ultimately improving clinical outcomes and operational efficiency. Future work will focus on expanding real-time learning capabilities and exploring federated learning approaches to address data privacy concerns while maintaining high model accuracy.
References
1. Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: state-of-the-art and research challenges. Journal of Services and Applications, 1(1), 7-18.
2. Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial Intelligence-Enabled Healthcare Delivery. Journal of the Royal Society of Medicine, 112(1), 22–28.
3. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2020). A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications. IEEE Internet of Things Journal, 4(5), 1125-1142.
4. Kumar, N., Mallick, P. K. (2018). The Internet of Things: Insights into the Building Blocks, Component Interactions, and Architecture Layers. Procedia Computer Science, 132, 109-117.
5. Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019). Federated Machine Learning: Concept and Applications. ACM Transactions on Internet Intelligent Systems and Technology, 10(2), 12.
6. Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
