Cloud-Based Healthcare Analytics Platform

Abstract

The rapid growth of healthcare data has necessitated scalable and efficient platforms for storage, processing, and analysis. This paper presents a Cloud-Based Healthcare Analytics Platform designed to handle large volumes of heterogeneous medical data and support data-driven decision-making. The proposed system leverages cloud computing technologies to provide on-demand resources, enabling efficient data storage, processing, and real-time analytics. Machine learning and data mining techniques are integrated into the platform to extract meaningful insights from electronic health records, medical images, and patient-generated data. The framework supports predictive analytics, disease risk assessment, and clinical decision support while ensuring data security and privacy through encryption and access control mechanisms. Additionally, the platform enables interoperability and seamless data sharing across healthcare systems. Experimental observations indicate that the proposed solution improves scalability, reduces computational overhead, and enhances analytical performance compared to traditional systems. Integration with prior research in healthcare analytics and ensemble learning further strengthens the system’s robustness. The study highlights the potential of cloud-based platforms in transforming healthcare delivery by enabling efficient, cost-effective, and scalable analytics solutions.