Quantum-enhanced federated blockchain for privacy-preserving cardiovascular intelligence

Area of Science:

• Cardiovascular Health
• Health Informatics
• Artificial Intelligence in Medicine

Background:

• Cardiovascular Diseases (CvDs) are a leading global cause of mortality.
• Current healthcare systems struggle with fragmented data, weak security, and ineffective multi-modal data processing.
• There is a need for advanced, privacy-preserving risk stratification technologies.

Purpose of the Study:

• To propose a Decentralized Federated Blockchain for Cardiovascular Intelligence (DFBCI) framework.
• To address challenges in data security, integrity, and multi-modal data processing for CvD risk prediction.
• To enhance collaborative healthcare analytics while protecting patient privacy.

Main Methods:

• Integration of Multi-Chain Aggregation with Adaptive Consensus (MCAC) and Federated Dynamic Relational Learning (FDRL).
• Utilizing blockchain for secure data aggregation (medical and imaging) and integrity.
• Employing Quantum-Enhanced Privacy Masking (QEPM) for secure feature extraction and federated optimization for risk modeling.
• Decentralized validation using smart contracts.

Main Results:

• Achieved 19% improvement in CvD risk prediction accuracy compared to standard methods.
• Reduced model convergence time by 22%.
• Enhanced system scalability by 27%.

Conclusions:

• The DFBCI framework enables secure collaboration and advances CvD risk stratification globally.
• The system protects patient privacy rights, setting a benchmark for rapid, collaborative assessments.
• DFBCI facilitates powerful healthcare analytical platforms for improved cardiovascular intelligence.