Abstract
The thyroid gland plays a pivotal role in maintaining overall health, making the accurate diagnosis of thyroid diseases crucial. In this study, we propose an innovative approach to enhance the diagnostic accuracy of thyroid diseases through the integration of the Emperor Penguin Optimization Algorithm (EPO). EPO, inspired by the efficient foraging behavior of emperor penguins, offers a unique optimization strategy for feature selection and model tuning in medical diagnostics. By employing EPO, we optimize the selection of relevant diagnostic features and fine-tune the parameters of machine learning models.
 Our experimental results demonstrate a significant improvement in diagnostic accuracy compared to traditional methods. The EPO-enhanced Thyroid Disease Diagnosis model achieves superior performance, ensuring a higher true positive rate and a lower false positive rate. This promising outcome suggests that EPO can be a valuable tool in the development of more accurate and reliable diagnostic systems for thyroid diseases, potentially leading to early detection and improved patient outcomes.
 Our approach achieved an impressive accuracy rate of 99.7 % when tested.
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