Leveraging Cloud Computing for Real-Time Big Data Analytics in Healthcare Systems

Abstract

Electronic health records (EHRs), medical imaging, and wearable devices generate vast amounts of data in healthcare systems, necessitating scalable, real-time analytics. The proposed study recommends a cloud-based system that combines streaming ingestion and Apache Spark processing with machine learning-based predictive models and Gomoku-inspired stream-cipher encryption, along with blockchain validation. The proposed framework is designed to support continuous data ingestion, low-latency processing, and secure analytics in distributed healthcare environments. Experimental evaluation demonstrates that the system achieves a 96.5% F1-score, 120 ms processing time, 450 MB/s throughput, and 68% resource usage, outperforming Hadoop (90.2% F1-score, 350 ms) and Spark (94.1%, 200 ms) in processing latency, throughput, and resource efficiency. Findings further indicate that operational costs would be reduced by 30%, early intervention rates would improve by 85%, and patient engagement would increase by 40%, enabling the delivery of proactive and personalized healthcare. Additionally, integrating game-theoretic key generation with cloud-based analytics enhances data confidentiality and integrity for real-time healthcare applications. The proposed approach demonstrates the feasibility of combining scalable cloud computing, real-time big data analytics, and advanced security mechanisms to address the growing demands of modern healthcare systems.