Thyroid Diseases Detection Using Evolutionary Machine Learning and Deep Learning : A survey
DOI:
https://doi.org/10.29304/jqcsm.2025.17.22228Keywords:
Machine learning, Thyroid Disease, Evolutionary algorithms, Deep learningAbstract
This paper presents a survey of studies and research on machine learning techniques, deep learning, as well as research using optimization algorithms, and evolutionary algorithms, in relation to discoveries of diagnosing thyroid disorders. The paper includes an analysis of recent studies. The techniques of researchers in this field of diseases (thyroid diseases) are shown. As well as the results of the aforementioned studies on accuracy factors, Precision, Recall, F1-scor and. Advantages and disadvantages of advanced algorithms. By analyzing the current methods that were surveyed, it was proven that transfer learning techniques, as well as techniques that use optimization algorithms, are the most efficient. In some research, preprocessing plays a major role in obtaining better results in the model training stage.
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Copyright (c) 2025 Anwar Kadhem Kreem, Osama Majeed Hilal, Alaa Taima Albu-Salih
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