Article
Lightweight Secure and Transparent Ensemble Learning for IntegrityPreserved Health Records
The rapid evolution of digital healthcare solutions has created a critical need for systems that can manage Electronic Health Records (EHR) securely while supporting intelligent decision-making. Accurate prediction of heart disease and effective handling of patient data require both advanced analytical models and strong privacy protection, which traditional centralized systems fail to provide due to risks such as data breaches, unauthorized access, and lack of transparency. Moreover, these systems do not effectively combine predictive analytics with secure data sharing, limiting their use in real-time healthcare scenarios. To overcome these challenges, this work presents a secure healthcare framework that integrates Machine Learning (ML), blockchain technology, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE). The system processes a heart disease dataset using preprocessing techniques such as missing value handling, normalization with Standard Scaler, and traintest splitting, followed by prediction using Random Forest (RF) and Support Vector Machine (SVM), with performance evaluated through accuracy, precision, recall, and F1-score. A web-based interface is developed using Django to facilitate interaction between doctors and patients. For enhanced security, patient data is encrypted using Elliptic Curve Integrated Encryption Scheme (ECIES)-based CP-ABE before being stored on the blockchain through Web3, while smart contracts manage user registration, EHR storage, and controlled access. This integrated approach ensures secure data sharing, reliable prediction of heart conditions, and transparent record management, ultimately improving data security, prediction accuracy, and enabling decentralized, tamper-proof healthcare services
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