Exploring the Experimental Effectiveness of a Hybrid Deep Learning Approach in Detecting Thyroid Disease using Intelligent Predictions

In the realm of machine learning, addressing class imbalance is a prevalent challenge, especially in datasets where certain classes are underrepresented. Synthetic Minority Over-sampling Technique (SMOTE) emerges as a powerful solution, strategically generating synthetic instances for minority classes, thereby rebalancing the dataset. By carefully selecting parameters and employing appropriate evaluation metrics, SMOTE enhances the overall classification performance, ensuring accurate representation of minority classes essential for real-world applications. Bidirectional Feature Elimination (BiDFE) stands out as a sophisticated technique for this purpose. BiDFE iteratively identifies and retains the most relevant features pertinent to thyroid disease classification, striking a balance between adding informative features and removing redundant ones. This method enhances the interpretability and generalization of the model, ensuring its robustness and efficiency in handling thyroid disease data. The Proposed Model TDIP (Thyroid Disease Identification and Prediction), a novel hybrid approach integrating Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) algorithms. Leveraging LSTM's temporal learning capabilities and SVM's discriminative power, TDIP achieves an impressive accuracy of 97% in thyroid disease classification.