Thyroid Disease Prediction Using Selective Features and Machine Learning Techniques

This study explores an advanced approach to thyroid disease prediction using selective features and machine learning techniques. The proposed methodology uses Bayesian Search for hyperparameter tuning in combination with ensemble learning methods such as Random Forest, XGBoost, and Gradient Boosting to improve model performance. Traditional hyperparameter tuning methods such as grid search and random search are often computationally intensive and time-consuming. In contrast, Bayesian Search efficiently explores the hyperparameter space and reduces the number of iterations needed to find the optimal setting. Ensemble learning techniques, known for their ability to combine multiple algorithms to achieve higher accuracy and robustness, are used to further improve predictive performance. By combining Bayesian Search and ensemble learning, the study aims to create a reliable predictive model for thyroid disorder detection with a focus on improving accuracy and minimizing computational overhead. The results show that this integrated approach offers a powerful framework for enhancing the model's performance in predictive tasks, paving the way for its application in other medical diagnoses and beyond.