Secure Multi-Disease Detection using Federated Learning and Optimized Graph-Based Deep Feature Attention with Blockchain Support
Year: 2026
Type: article
Abstract: The rise of digital healthcare systems has generated large, fragmented medical data for enhanced illness prediction. However, modern centralized machine learning algorithms distribute sensitive patient data, raising privacy, security, and legal concerns. Federated learning allows collaborative model training while protecting raw healthcare records, but heterogeneous medical data distributions, limited feature representation, and insufficient secure and reliable model-sharing mechanisms among healthcare institutions often present challenges. This study proposes a Blockchain-Enabled Federated Learning (BCFL) framework for privacy-preserving multi-disease prediction by integrating several complementary optimization and deep learning techniques. The framework employs the Humboldt Squid Optimization Algorithm (HSOA) for optimal feature selection, the Geometric Algebra Transformer (GATr) for enhanced feature representation, the Federated Graph Multilayer Edge Attention Network (FGMEAN) for graph-aware federated disease prediction, and the Great Wall Construction Algorithm (GWCA) for adaptive hyperparameter optimization. Blockchain technology is incorporated to strengthen security and data integrity within the federated learning environment, thereby supporting secure and privacy-preserving multi-disease prediction. The proposed framework is evaluated using the Heart Disease, Diabetes, and Chronic Kidney Disease datasets. The experimental results demonstrate prediction accuracies of 99.3%, 99.5%, and 99.3% for heart disease, diabetes, and chronic kidney disease, respectively. The framework also lowered latency, communication overhead, execution efficiency, and security compared to current techniques. Component-wise study shows that feature optimization, geometric feature embedding, graph-aware federated learning, and adaptive hyperparameter tuning improve predictive performance. Experimental results show that the BCFL-FGMEAN-GWCA framework provides a secure, privacy-preserving, and precise multi-disease prediction solution in distributed healthcare settings, enabling next-generation intelligent healthcare systems.
Authors: Krishna Prasad N Rao, Selvan Chinnaiyan
Institution: REVA University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7225122426