Blockchain-Driven Predictive Analytics for Diabetes and Cardiovascular Disease Management

Abstract

A unique method for improving predictive analytics and protecting medical data is to combine blockchain technology with machine learning. Novel approaches to early detection and treatment are necessary for non-communicable diseases like diabetes and cardiovascular conditions. In order to predict diabetes and cardiovascular diseases, this article suggests a safe framework that combines blockchain technology with machine learning methods. To guarantee that only authorized users can decrypt and access Electronic Medical Records (EMRs), the system uses smart contracts on the Ethereum blockchain to automatically enforce data access permissions. Transparent and secure data sharing between healthcare organizations is made possible by this integration, which also ensures data integrity, privacy, and regulated accessibility. Blockchain-stored data is subjected to a number of machines learning classifiers, such as XG Boost and Random Forest, in order to achieve high disease prediction accuracy through thorough performance evaluation and optimized parameter tuning. The architecture exhibits scalability and robustness, enhances patient privacy, and enables personalized therapies. The effectiveness of this combined approach is demonstrated by experimental results, underscoring its potential for practical implementation in decentralized healthcare ecosystems.