Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository, both of which undergo preprocessing steps, including outlier removal, addressing missing data, data cleansing, and feature reduction. Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100% and 99.86% accuracy in identifying hyperthyroidism and hypothyroidism, respectively. Beyond enhancing detection accuracy, the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications. It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme. This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.