Integrating Predictive Classification of Big Data in Cloud Environments with Blockchain for Secure Electronic Health Records Management in Healthcare

Abstract

This paper explores how to blend predictive classification techniques for big data in cloud computing with blockchain technology to boost the safety and usability of Electronic Health Records (EHRs) in healthcare. Given the massive amounts of data generated daily, managing storage, analysis, and protection becomes quite challenging. Though clouds handle large volumes well, security remains a sticking point. Our proposed approach involves using cloud platforms to execute machine learning methods such as Random Forest and SVM for analyzing health data. At the same time, blockchain ensures secure transactions, preserving patient information. This setup allows for real-time sorting to predict illnesses and assess risks while recording everything safely via blockchain. Simulations demonstrated accuracy as high as 92%, enhanced by encryption and access controls. This combo tackles significant challenges like data breaches, integrating disparate systems, and scaling up. In sum, it provides practical guidance for establishing secure and efficient health data systems.