Enhancing Thyroid Disease Prediction: Integrating Machine Learning Models for Improved Diagnostic Accuracy and Interpretability

Advancements in machine learning (ML) offer groundbreaking opportunities for medical diagnostics, particularly in thyroid disease prediction. This study presents a comprehensive evaluation and integration of three ML models: logistic regression, random forest, and gradient boosting trees, each chosen for their relevance to handling various aspects of medical data analysis, including imbalance and specificity in disease categorization. Emphasizing model interpretability and diagnostic accuracy, we introduce a novel hybrid approach incorporating concept drift detection to ensure model adaptability over time. By conducting an in-depth analysis using a significant dataset from the UCI Machine Learning Repository, our work not only showcases the individual strengths of these models but also demonstrates the enhanced performance and reliability of the hybrid model in predicting thyroid diseases amidst evolving data patterns. This research aims to bridge the gap between ML advancements and practical medical applications, offering insights into improving diagnostic processes and patient outcomes in thyroid-related health issues.