Secure and Adaptive Prediction of Post-TAVR Outcomes: Integrating Federated Learning and Blockchain for Enhanced Patient Care

Abstract

This paper presents a novel health data analysis platform for improved individualized risk forecasting of permanent pacemaker implantation (PPI) following transcatheter aortic valve replacement (TAVR) procedures. Specifically, we introduce XGBoost-federated adaptive interpolation transfer learning (XG-FedAIT)—a platform that integrates heterogeneous hospital data sets via adaptive output-level interpolation and performance-weighted model ensembling. This approach facilitates federated learning across institutions with different feature spaces and prediction targets, such as time-to-event and binary models, eliminating structural and semantic mismatches prevalent in regular federated transfer learning. To ensure secure, privacy-preserving, and general data protection regulation (GDPR)-compliant data exchange, we propose a dual-chain blockchain architecture integrating proof of authority (PoA), zero-knowledge proofs (ZKPs), and chain-specific smart contracts, and off-chain encrypted storage through Filecoin. Experimental evaluation proves that the PrimaryChain handles over 300 tx/s with a median latency of 500 ms, and the SecondaryChain offers 99.8% data availability and decentralized access control. The system is scalable to handle up to 2,500 transactions/hour, and the federated learning pipeline classifies PPI risk with an F1-score of 0.85 and AUROC of 0.91. These results support the effectiveness of our system in delivering real-time, regulation-compliant, and clinically actionable cardiac care in distributed environments.