(untitled) (Baghdad Science Journal, 2024)
People with underactive thyroids frequently endure severe symptoms. Correct classification and machine learning substantially improve thyroid disease diagnosis. This precise classification will impact the timely delivery of care to the patients. Although diagnostic techniques exist, they frequently seek binary categorization, use insufficiently big datasets, and lack confirmation of their conclusions. The focus of current approaches is on model optimisation, whereas feature engineering is neglected. This research presents the Adaptive Elephant Herd Optimisation Algorithm (AEHOA) model for selecting optimal attributes in order to circumvent these limitations. At first, employ a method called the Synthetic Minority Over-sampling Technique (SMOTE) to even out the data. Finally, the parameters of the AEHOA model are fed into a Convolutional Neural Network (CNN) to categorise data and enhance prediction. The accuracy of classification predictions was also increased by tweaking the dataset. Both datasets were put through a categorization process for a more precise comparison of results.