Analyzing the Impact of Data Imputation Techniques on Predictive Model Performance in Healthcare

1. Introduction 2. Background and Motivation 2.1 Importance of Data Imputation 2.2 Predictive Modeling in Healthcare 2.3 Challenges in Handling Missing Data 3. Literature Review 3.1 Overview of Data Imputation Techniques 3.2 Comparison of Existing Studies 3.3 Gap Analysis in Current Research 4. Methodology 4.1 Research Design 4.2 Data Collection Process 4.3 Imputation Methods Evaluated 4.4 Metrics for Model Performance 5. Data Imputation Techniques 5.1 Simple Imputation Methods 5.2 Advanced Machine Learning Techniques 5.3 Hybrid Approaches in Imputation 6. Predictive Model Development 6.1 Selection of Models 6.2 Training and Testing Procedures 6.3 Evaluation of Model Accuracy 7. Results and Discussion 7.1 Impact on Model Performance 7.2 Comparative Analysis of Techniques 7.3 Implications for Healthcare Applications 8. Conclusion and Future Work 8.1 Summary of Key Findings 8.2 Limitations of the Study 8.3 Directions for Future Research