MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange
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K. Hariprasath
Abstract
Balancing interoperability of data, security, and patient privacy is an important need for the healthcare industry, particularly for distributed care systems. Centralized architectures are vulnerable to latency, privacy laws violations, and loss of data. We introduce MediChain-FL, a new blockchain-federated learning approach for privacy-preserving, decentralized, and secure healthcare data analysis and sharing as a solution to these issues. MediChain-FL facilitates real-time multiparty model training over encrypted patient information without centralized data collection by integrating federated learning with a proof-of-authority (PoA) blockchain. We merge self-sovereign identity (SSI) with verifiable credentials (VCs) and decentralized identifiers (DIDs) to better preserve identity privacy and access control. Smart contracts provide compensation schemes for participating nodes, access audits, and training participation enforcement. In imitation of a federated real-world environment, our test environment had 1,200 patient records from the PIMA Indian Diabetes Dataset, distributed in six healthcare facilities. The MediChain-FL system they proposed outperformed traditional centralized models by 5.6%, and its model accuracy was 93.2% in predicting diabetes at an early stage. Further, the solution ensured 100% locality of patient data, lowered the probability of a data breach by 91.7%, ensured blockchain consensus latency of less than 1.3 s, and charged as little as $0.004 per data access. These results demonstrate that MediChain-FL facilitates the exchange of healthcare data that is secure, regulated, and intelligent, which opens the door for secure and scalable AI-driven diagnostics.
Abstract
Balancing interoperability of data, security, and patient privacy is an important need for the healthcare industry, particularly for distributed care systems. Centralized architectures are vulnerable to latency, privacy laws violations, and loss of data. We introduce MediChain-FL, a new blockchain-federated learning approach for privacy-preserving, decentralized, and secure healthcare data analysis and sharing as a solution to these issues. MediChain-FL facilitates real-time multiparty model training over encrypted patient information without centralized data collection by integrating federated learning with a proof-of-authority (PoA) blockchain. We merge self-sovereign identity (SSI) with verifiable credentials (VCs) and decentralized identifiers (DIDs) to better preserve identity privacy and access control. Smart contracts provide compensation schemes for participating nodes, access audits, and training participation enforcement. In imitation of a federated real-world environment, our test environment had 1,200 patient records from the PIMA Indian Diabetes Dataset, distributed in six healthcare facilities. The MediChain-FL system they proposed outperformed traditional centralized models by 5.6%, and its model accuracy was 93.2% in predicting diabetes at an early stage. Further, the solution ensured 100% locality of patient data, lowered the probability of a data breach by 91.7%, ensured blockchain consensus latency of less than 1.3 s, and charged as little as $0.004 per data access. These results demonstrate that MediChain-FL facilitates the exchange of healthcare data that is secure, regulated, and intelligent, which opens the door for secure and scalable AI-driven diagnostics.
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
Chapters in this book
- Frontmatter I
- Contents V
- Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
- DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
- Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
- A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
- Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
- Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
- Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
- EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
- Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
- Machine Learning Techniques for Healthcare 193
- Applications and Benefits of Machine Learning in Healthcare 215
- Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
- Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
- Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
- MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
- On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
- Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
- AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
- A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
- Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
- Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
- Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
- MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
- Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
- Index
