In recent years, machine learning (ML) has become a pivotal tool for predicting and diagnosing thyroid disease. While many studies have explored the use of individual ML models for thyroid disease detection, the accuracy and robustness of these single-model approaches are often constrained by data imbalance and inherent model biases. This study introduces a filter-based feature selection and stacking-based ensemble ML framework, tailored specifically for thyroid disease detection. This framework capitalizes on the collective strengths of multiple base models by aggregating their predictions, aiming to surpass the predictive performance of individual models. Such an approach can also reduce screening time and costs considering few clinical attributes are used for diagnosis. Through extensive experiments conducted on a clinical thyroid disease dataset, the filter-based feature selection approach and the ensemble learning method demonstrated superior discriminative ability, reflected by improved receiver operating characteristic-area under the curve (ROC-AUC) scores of 99.9%. The proposed framework sheds light on the complementary strengths of different base models, fostering a deeper understanding of their joint predictive performance. Our findings underscore the potential of ensemble strategies to significantly improve the efficacy of ML-based detection of thyroid diseases, marking a shift from reliance on single models to more robust, collective approaches.
