Thyroid disease detection using enhanced extreme learning machine based on drop-connect method

The long-term physiologic effects of thyroid problems make them one of the most important endocrine disorders. Even if a lot of machine learning and deep learning techniques have been presented out for the early detection of thyroid disease, it is still difficult to achieve reliable and clinically accurate multi-class diagnostic performance. In this work, we suggest an Enhanced Extreme Learning Machine (EELM) that uses Drop-Connect regularization to enhance generalization and reduce over-fitting that is frequently seen in traditional ELM models. The pipeline for the suggested framework consists of seven steps: data preprocessing, model building, training, and evaluation. To simulate a clinically relevant diagnostic scenario, the model was assessed on a unified four-class thyroid classification task (hypothyroidism, hyperthyroidism, sick-euthyroid, and normal). The suggested EELM demonstrated steady and reliable multi-class performance with an average accuracy of approximately 82% under 10-fold cross-validation. The model achieved up to 99.89% accuracy in comparative binary classification studies (e.g., hypothyroid vs. normal), indicating the better division of some thyroid diseases. Accuracy, precision, recall, specificity, sensitivity, F1-score, ROC, and AUC measures were used to evaluate performance. The suggested method's robustness and significance were validated statistically using ANOVA and paired t-tests. Significant improvements over baseline models were confirmed by statistical validation with paired t-tests and ANOVA (p < 0.05). Overall, the findings show that the suggested EELM offers a clinically applicable, statistically supported, and computationally effective method for classifying thyroid diseases.