Citation

Riaz, Aisha and Wahid, Fazli and Ali, Sikandar and Jan, Salman and Lee, It Ee and Salhi, Amina and Alfaidi, Arij and Alkhayyat, Ahmed (2026) Thyroid disease detection using enhanced extreme learning machine based on drop-connect method. Scientific Reports, 16 (1). ISSN 2045-2322

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	31 Jul 2026 05:31
Last Modified:	31 Jul 2026 05:31
URII:	http://shdl.mmu.edu.my/id/eprint/16400

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