Advances in Blockchain and Machine Learning for Privacy-Preserving Healthcare Data Processing: A New Secure Analytics Framework

Abstract

Hospitals, researchers, and providers are increasingly creating and exchanging sensitive medical data due to the rapid digitization of the healthcare system. While healthcare data processing and storage jeopardize privacy, security, and integrity, advanced data analytics enhance clinical decision-making and patient care. The security and trustworthiness of a centralized healthcare data management system’s patients are vulnerable to cyberattacks, illegal access, and data breaches. For the purpose of protecting patients’ personal health information, this study presents a blockchain-machine learning analytics architecture. Blockchain technology ensures the security, transparency, and tracking of medical records through its distributed, immutable ledger. But machine learning helps with clinical decision support, predictive modeling, and enhanced data analysis from huge healthcare datasets. Without disclosing patient information, the proposed technology enables machine learning models to extract useful insights from encrypted healthcare data. In order to store and analyze patient data, the framework employs distributed authentication, secure smart contracts, and encrypted data access protocols. Data security, transparency of healthcare data transactions, and scalable medical research and diagnostic analytics are all improved by the suggested solution, according to performance evaluation. Integrating blockchain technology with machine learning has the potential to make digital healthcare infrastructures safer, more reliable, and more adept at handling patient data.