Thyroid Disease Detection Using Machine Learning

Mohammad Faiz; Syed Wajahat Abbas  Rizvi

doi:10.54060/jase.v3i2.32

Authors

Mohammad Faiz Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India
Dr. Syed Wajahat Abbas Rizvi Amity School of Engineering and Technology Lucknow, Amity University Uttar Pradesh, India

DOI:

https://doi.org/10.54060/jase.v3i2.32

Keywords:

Machine Learning, Thyroid Disease Detection, Decision Tree, Random Forest

Abstract

The thyroid gland, which is in charge of controlling metabolism and other biological functions, is affected by thyroid illness, a frequent medical disorder. Successful thyroid disease management and therapy depends on early diagnosis and treatment. Recent years have seen the development of numerous machine learning methods and artificial intelligence (AI) algorithms to help with the early detection and diagnosis of thyroid disease. These methods entail evaluating a range of patient data, such as laboratory results, imaging studies, and clinical complaints. These algorithms can find patterns and correlations in vast volumes of patient data that might not be obvious to human ex-perts. This may result in earlier identification and more precise diagnosis of thyroid ill-ness, enhancing patient outcomes and lowering medical expenses. Additional study and development are necessary to improve these methods and incorporate them into clinical practice.

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