An Integrated Analysis of Deep Learning and Machine Learning for Thyroid Disease Detection

There has been a rapid implementation of artificial intelligence technologies in health care systems. Neural networks, machine learning, deep learning, and other types of learning are the obvious next step in the automation that more powerful computers enable. Massive gains in efficiency, particularly in business and internet technologies, have already ushered artificial intelligence into everyday life [1] The most frequent area of application in this context is early illness detection. Thyroid disorders are an example of a condition that, in this case, can be successfully treated if found very early. The motive of this literature is to give results from the analysis of several methods and criteria for the accurate identification of thyroid disease.