An Ensemble Machine Learning Approach with Hybrid Feature Selection Technique to Detect Thyroid Disease

Thyroid disease is a prevalent health problem that requires early detection for effective treatment. However, there is no universal model for detecting thyroid abnormalities efficiently. This study proposes a three-layer thyroid disease detection framework that utilizes different feature engineering techniques to explore various thyroid datasets, improving model performance. We propose a hybrid framework to identify the most relevant features utilizing feature selection techniques contributing significantly to the model’s performance. We evaluate the proposed bagging XGBoost ensemble model’s performance against K-nearest neighbors, extreme learning machines, and random forest classifiers. It surpassed all with 98.44% accuracy with only 53.57% of the dataset’s features. The proposed model outperformed other classifiers with 98.65% accuracy during cross-validation on the second dataset. Stress testing with train–test split ratios of 70–30 and 30–70 produced 94.92% and 94.68% accuracy rates, respectively, that also outperform the benchmark ML models. These findings have significant implications for improving thyroid disease diagnosis using machine learning techniques.