A federated incremental blockchain framework with privacy preserving XAI optimization for securing healthcare data
8.0
来源：
Nature
关键字：
digital twin
发布时间：
2025-10-31 03:56
摘要：
The study presents a novel framework, PPFILB-OXAI, which integrates blockchain, federated incremental learning, and explainable AI to enhance the security and privacy of healthcare data. The framework effectively addresses challenges in data privacy while achieving high accuracy in disease classification tasks, specifically for breast cancer and heart disease. The results indicate significant improvements in model performance, demonstrating the potential for real-world applications in healthcare settings.
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价值分投票
评分标准
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价值维度分析
domain_focus
1.0分+1.0分+0.5分=2.5分
business_impact
0.8分
scientific_rigor
1.5分+1.0分=2.5分
timeliness_innovation
1.5分
investment_perspective
2.5分
market_value_relevance
1.0分
team_institution_background
0.5分
technical_barrier_competition
1.0分
关键证据
The proposed model achieves high accuracy results of 97.93% and 96.77% for heart disease and breast cancer datasets respectively.
The integration of blockchain technology ensures secure and transparent healthcare data management.
The framework addresses critical challenges in federated learning, enhancing model robustness and privacy.
真实性检查
否
AI评分总结
The study presents a novel framework, PPFILB-OXAI, which integrates blockchain, federated incremental learning, and explainable AI to enhance the security and privacy of healthcare data. The framework effectively addresses challenges in data privacy while achieving high accuracy in disease classification tasks, specifically for breast cancer and heart disease. The results indicate significant improvements in model performance, demonstrating the potential for real-world applications in healthcare settings.