Evaluating Fairness and Bias Mitigation Techniques in Machine Learning Algorithms for Healthcare Data

1. Introduction 1.1 Background and Motivation 1.2 Scope of the Study 1.3 Research Objectives 1.4 Structure of the Paper 2. Understanding Fairness in Machine Learning 2.1 Definitions of Fairness 2.2 Importance in Healthcare 2.3 Common Challenges 3. Bias in Healthcare Data 3.1 Sources of Bias 3.2 Types of Bias 3.3 Impact on Outcomes 4. Existing Bias Mitigation Techniques 4.1 Pre-processing Methods 4.2 In-processing Approaches 4.3 Post-processing Strategies 5. Evaluation Frameworks for Fairness 5.1 Criteria for Evaluation 5.2 Metrics and Benchmarks 5.3 Limitations and Considerations 6. Case Studies in Healthcare 6.1 Case Study A: Diagnosis Systems 6.2 Case Study B: Treatment Recommendations 6.3 Case Study C: Risk Prediction Models 7. Comparative Analysis of Techniques 7.1 Strengths and Weaknesses 7.2 Applicability to Healthcare 7.3 Lessons Learned 8. Conclusion and Future Directions 8.1 Summary of Findings 8.2 Recommendations for Practice 8.3 Directions for Further Research