Blockchain Pervasive Ledger Technology for Healthcare Using Rough Set Theory

Abstract

Managing and securing patient data in the healthcare sector is critical. While existing research on integrating blockchain technology with healthcare systems shows promise in enhancing data security and privacy, challenges in data analysis and decision-making accuracy persist. Traditional methods often struggle with imprecise and incomplete data, resulting in unreliable outcomes. This paper proposes a novel framework that integrates blockchain pervasive ledger technology with rough set theory to address these issues. By leveraging the immutable and transparent nature of blockchain and the data analysis capabilities of rough set theory, we aim to improve data security, privacy, and decision-making in healthcare. The proposed framework employs the Apriori algorithm for rule extraction and the Proof of Stake (PoS) consensus mechanism for blockchain transactions. We demonstrate the framework's effectiveness using a real-world dataset from the MIMIC-III (Medical Information Mart for Intensive Care III) database, which includes detailed patient records such as symptoms, test results, diagnoses, and treatment outcomes. The system architecture and mathematical foundations are discussed, along with empirical results showcasing enhanced decision-making accuracy and data security. Our results indicate a significant improvement in data analysis and security, with a 92% accuracy rate in medical diagnoses compared to traditional systems. This research contributes to the development of more reliable and secure healthcare data management systems, ultimately improving patient care and outcomes.