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Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data

  • Dafik , N. Ganitha Aarthi , Bura Vijay Kumar , Alycia Sebastian , S. Bathrinath and Disha Sushant Wankhede
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Machine Learning in Healthcare
This chapter is in the book Machine Learning in Healthcare

Abstract

Over the past few years, the use of machine learning (ML) in healthcare has generated unprecedented potential for disease prediction, treatment personalization, and patient outcome enhancement. Nevertheless, the sensitive contents of clinical data and stringent privacy laws like HIPAA and GDPR restrict the central collection of data and hinder collaborative model training. In response to this, we introduce an innovative privacy-protection approach in the form of federated learning (FL), augmented with differential privacy, secure multiparty computation, and adaptive model training. In this scenario, each individual healthcare institution owns its data and shares encrypted updates of the model only, guaranteeing that the patient records do not become known. Our system design comprises distributed clients (e.g., hospitals) and a central coordination server that compiles encrypted updates to construct a strong global model. The approach also incorporates personalization methods and clustering approaches to address non-IID data distributions typical in clinical datasets. Experimental assessments on synthetic and real-world electronic health record datasets prove that the proposed approach yields competitive performance (accuracy: 89.9%, AUC-ROC: 0.92) with considerable privacy loss mitigation (ε = 2.1). Our method is in contrast to conventional FL and centralized ML models, yet it yields an optimal trade-off among utility and security. It is therefore appropriate for critical tasks such as early disease diagnosis and treatment outcome modeling. The suggested framework builds trust and cooperation between institutions without infringing on personal privacy, opening the door to scalable, ethical, and regulatory-compliant artificial intelligence adoption in healthcare.

Abstract

Over the past few years, the use of machine learning (ML) in healthcare has generated unprecedented potential for disease prediction, treatment personalization, and patient outcome enhancement. Nevertheless, the sensitive contents of clinical data and stringent privacy laws like HIPAA and GDPR restrict the central collection of data and hinder collaborative model training. In response to this, we introduce an innovative privacy-protection approach in the form of federated learning (FL), augmented with differential privacy, secure multiparty computation, and adaptive model training. In this scenario, each individual healthcare institution owns its data and shares encrypted updates of the model only, guaranteeing that the patient records do not become known. Our system design comprises distributed clients (e.g., hospitals) and a central coordination server that compiles encrypted updates to construct a strong global model. The approach also incorporates personalization methods and clustering approaches to address non-IID data distributions typical in clinical datasets. Experimental assessments on synthetic and real-world electronic health record datasets prove that the proposed approach yields competitive performance (accuracy: 89.9%, AUC-ROC: 0.92) with considerable privacy loss mitigation (ε = 2.1). Our method is in contrast to conventional FL and centralized ML models, yet it yields an optimal trade-off among utility and security. It is therefore appropriate for critical tasks such as early disease diagnosis and treatment outcome modeling. The suggested framework builds trust and cooperation between institutions without infringing on personal privacy, opening the door to scalable, ethical, and regulatory-compliant artificial intelligence adoption in healthcare.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
https://doi.org/10.1515/9783111541235-007

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Early Prediction of Chronic Kidney Disease Using a Novel Hybrid Regularized Adaptive Boosting Algorithm: An Advanced Machine Learning Approach 1
  4. DigiCure: A Patient-Centric Framework for Digital Transformation in Healthcare 21
  5. Exploring Machine Learning Approaches for Maximizing the Likelihood of Diabetes Classification 41
  6. A Hybrid Machine Learning Model for Risk Stratification and Functional Outcome Prediction in Stroke Survivors 61
  7. Data-Driven Machine Learning Strategies for Oncological Disease Prediction and Early-Stage Detection 83
  8. Machine Learning Applications in Mental Health: Ensemble-Based Predictive Modeling for Depression and Anxiety detection 103
  9. Privacy-Preserving Machine Learning in Clinical Research: Using Federated Learning to Protect Patient Data 129
  10. EpiCastNet: A Spatiotemporal Hybrid Learning Framework for Real-Time Epidemic Forecasting 149
  11. Machine Learning for Early Detection of Chronic Diseases: A Case Study in Diabetes Prediction 171
  12. Machine Learning Techniques for Healthcare 193
  13. Applications and Benefits of Machine Learning in Healthcare 215
  14. Intelligent Treatment Recommendation Using CareRecNet: A Patient-Centered Approach to Digital Health Transformation 233
  15. Reinforcement-Driven Graph Neural Framework for Personalized and Proactive Patient Care in Digital Health Systems 251
  16. Hybrid Attention-Driven Network for Predictive Healthcare Using Machine Learning and Data Analytics Perspective 271
  17. MSAG-DFE: A Multi-scale Attention-Guided Deep Feature Extraction Framework for Enhanced Medical Image Diagnostics 287
  18. On Mental Health Monitoring Using Commercial Wearable Devices and Machine Intelligence 305
  19. Enhancing Healthcare Delivery Through Evidence-Based Data Utilization 335
  20. AGBO-CP: An Adaptive Gradient Boosted Optimization Framework for Enhanced Clinical Prediction Accuracy 367
  21. A Hierarchical Cross-Fusion Feature Extraction Network for Accurate Cervical Cancer Classification Using Cytology Images 387
  22. Analyzing the Impact of Social Network on Epidemiological Spread in the Healthcare Sector 409
  23. Intelligent Interventions: Practical Applications of Machine Learning for Data-Driven Decision-Making in Healthcare 431
  24. Stress Recognition Through Physiological and Behavioral Signals: A Machine Learning Perspective 453
  25. MediChain-FL: A Federated Blockchain Framework for Privacy-Preserving and Intelligent Healthcare Data Exchange 485
  26. Reinforced Multi-objective Optimization Framework for Adaptive Healthcare Decision Intelligence 503
  27. Index
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