Exploring Machine Learning Approaches for Predicting Autoimmune Disease Flare-Ups

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Structure of the Thesis 2. Overview of Autoimmune Diseases 2.1 Definition and Characteristics 2.2 Common Autoimmune Diseases 2.3 Impact on Patients and Society 3. Machine Learning Fundamentals 3.1 Definition and Concepts 3.2 Types of Machine Learning Algorithms 3.3 Evaluation Metrics for Models 4. Predictive Modeling Techniques 4.1 Supervised Learning Methods 4.2 Unsupervised Learning Methods 4.3 Reinforcement Learning Basics 5. Data Collection and Preprocessing 5.1 Data Sources and Acquisition 5.2 Data Cleaning Techniques 5.3 Feature Selection and Engineering 6. Case Study: Application to Lupus 6.1 Lupus Disease Overview 6.2 Model Selection Criteria 6.3 Results and Analysis 7. Challenges and Limitations 7.1 Data Availability and Quality 7.2 Model Complexity and Interpretability 7.3 Ethical Considerations in Predictions 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Recommendations for Researchers 8.3 Potential Future Directions