Evaluating Bias in Machine Learning Models for Predictive Analytics in the Healthcare Sector

1. Introduction 1.1 Background of Predictive Analytics 1.2 Importance of Machine Learning in Healthcare 1.3 Objectives of the Study 2. Literature Review 2.1 Overview of Bias in Machine Learning 2.2 Previous Studies on Healthcare Applications 2.3 Identified Gaps in Current Research 3. Methodology 3.1 Research Design 3.2 Data Collection Methods 3.3 Analytical Techniques Used 4. Types of Bias in Models 4.1 Data Bias 4.2 Algorithmic Bias 4.3 Interpretation Bias 5. Case Studies in Healthcare 5.1 Case Study: Predictive Diagnostics 5.2 Case Study: Treatment Outcomes 5.3 Case Study: Patient Risk Assessment 6. Tools to Evaluate Bias 6.1 Statistical Tests 6.2 Visualization Techniques 6.3 Software and Frameworks 7. Mitigation Strategies 7.1 Data Preprocessing Techniques 7.2 Algorithmic Adjustments 7.3 Policy Recommendations 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Implications for Healthcare Practice 8.3 Directions for Future Research