Predictive Analysis of Early Thyroid Disorders Using Integration of Data Mining and Ensemble Intelligence Approaches

This study proposes a novel machine learning (ML) approach for early detection of thyroid disorders using data mining and ensemble learning techniques. By leveraging a diverse dataset—including demographic details, medical history, symptoms, and diagnostic test results—a high-precision model is developed to predict the risk of thyroid dysfunction. The methodology integrates self-adaptive stacking, weighted metafeatures, and Bayesian optimization to enhance model performance. After preprocessing, feature selection, and correlation analysis, various ensemble classifiers are trained and evaluated. Ensemble methods improve prediction accuracy by combining multiple base models, making them well suited for complex medical classification tasks. The proposed model achieved outstanding performance, with an accuracy of 99.46%, sensitivity (recall for class 1) of 99.85%, specificity (recall for class 0) of 94.83%, and an overall F1-score of 99.71%. The slight variation in class-wise F1-scores reflects the impact of class imbalance, where metrics tend to favor the majority class. Nevertheless, these metrics collectively underscore the model’s robustness in effectively identifying at-risk individuals while minimizing false positives and false negatives. Heat maps and other visualization tools were used to interpret patterns and improve model transparency. The findings support the potential of data-driven approaches to enhance early diagnosis, inform personalized treatment plans, and improve clinical decision-making. This work not only contributes to better thyroid disease detection but also advances the development of robust, interpretable, and scalable ML models for healthcare applications.