Thyroid Disease Classification using Machine Learning Algorithms

¹ Department of AIMLE, GRIET, Hyderabad, Telangana State, India
² Associate Professor, Department of CSE, GPCET, Kurnool, India
³ Student, Department of CSE, GPCET, Kurnool, India
⁴ Student, Department of CSBS, GRIET, Hyderabad, Telangana State, India Department of AIMLE, GRIET, Hyderabad, Telangana State, India
⁵ Student, G Pullaiah College of Engineering and Technology, Kurnool, India

^* Corresponding author: ramkumar1695@grietcollege.com

Abstract

Thyroid gland is one of the body’s most important glands because it regulates the metabolism of the human body. It controls how the body works by releasing specific hormones into the blood. The two different hormone disorders are hypothyroidism and hyperthyroidism. When these disorders occur, the thyroid gland releases a particular hormone into the blood that regulates the metabolism of the body. Iodine deficiency, autoimmune conditions, and inflammation can contribute to thyroid issues. The disease is diagnosed using a blood test, but there is frequently some noise and disturbance. Techniques for cleaning data can be used to make it simple enough to perform analytics that show the patient's risk of developing thyroid disease. This paper deals with the analysis and classification models used in thyroid disease based on the information gathered from the dataset taken from the UCI machine learning repository. Machine learning plays a crucial role in the detection of thyroid disease. This paper suggests various machine-learning methods for thyroid detection and diagnosis for thyroid prevention.