Recondite Thyroid Pathology Prediction: Hermeneutic Integration of Neural and Machine Learning Architectures

Thyroid issues are increasingly prevalent, making early detection crucial for reducing mortality and complications. Accurate disease progression estimation and understanding clinical feature interactions are essential for effective medical diagnosis and treatment. Our study addresses these challenges by employing machine learning and deep learning models with advanced clinical feature analysis and ensemble-learning techniques. This approach aids in predicting disease progression and assessing the interdependence of clinical features. We utilized five models-K-Nearest Neighbors (KNN), XGBoost, Random Forest, Convolutional Neural Network (CNN), and TabNet-to identify the best thyroid prediction outcomes. Our experimental results indicate that the proposed models significantly outperform traditional methods. The CNN model achieved the highest accuracy ($\mathbf{9 8. 7 0 \%}$) and AUC ($\mathbf{1 0 0 \%}$), followed by TabNet with an accuracy of $\mathbf{9 7. 4 0 \%}$ and AUC of $\mathbf{9 9. 1 8 \%}$, demonstrating superior performance in complex classification tasks. The ensemble ML classifier combining multiple models achieved $100 \%$ sensitivity and $\mathbf{9 9. 7 1 \%}$ accuracy. By effectively addressing feature reduction and high-class imbalance, our ensemble approach excels in handling classification challenges, confirming its effectiveness in thyroid disease prediction.