E-ISSN 0976-2833 | ISSN 0975-3583
 

Research Article 


A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip.

Abstract
The Thyroid gland is a vascular gland and one of the most important organs of a human body. This
gland secretes two hormones which help in controlling the metabolism of the body. Thyroid disease is a major cause
of formation in medical diagnosis and in the prediction, onset to which it is a difficult axiom in the medical research.
The two types of Thyroid disorders are Hyperthyroidism and Hypothyroidism. When this disorder occurs in the
body, they release certain type of hormones into the body which imbalances the body’s metabolism. Thyroid related
Blood test is used to detect this disease but it is often blurred and noise will be present. Data cleansing methods were
used to make the data primitive enough for the analytics to show the risk of patients getting this disease. The
machine learning plays a decisive role in the process of disease prediction and this paper handles the analysis and
classification models that are being used in the thyroid disease based on the information gathered from the dataset
taken from UCI machine learning repository. In this paper few machine learning techniques for diagnosis and
prevention of thyroid.

Key words: Thyroid Disease, Naïve Bayse, kNN, Decision Tree, Machine Learning Algorithms


 
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How to Cite this Article
Pubmed Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. J Cardiovasc. Dis. Res.. 2021; 12(5): 1057-1065. doi:10.31838/jcdr.2021.12.05.139


Web Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. http://www.jcdronline.org/?mno=124388 [Access: September 14, 2021]. doi:10.31838/jcdr.2021.12.05.139


AMA (American Medical Association) Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. J Cardiovasc. Dis. Res.. 2021; 12(5): 1057-1065. doi:10.31838/jcdr.2021.12.05.139



Vancouver/ICMJE Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. J Cardiovasc. Dis. Res.. (2021), [cited September 14, 2021]; 12(5): 1057-1065. doi:10.31838/jcdr.2021.12.05.139



Harvard Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip (2021) A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. J Cardiovasc. Dis. Res., 12 (5), 1057-1065. doi:10.31838/jcdr.2021.12.05.139



Turabian Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. 2021. A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. Journal of Cardiovascular Disease Research, 12 (5), 1057-1065. doi:10.31838/jcdr.2021.12.05.139



Chicago Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. "A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES." Journal of Cardiovascular Disease Research 12 (2021), 1057-1065. doi:10.31838/jcdr.2021.12.05.139



MLA (The Modern Language Association) Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip. "A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES." Journal of Cardiovascular Disease Research 12.5 (2021), 1057-1065. Print. doi:10.31838/jcdr.2021.12.05.139



APA (American Psychological Association) Style

Dr. N. Baggyalakshmi, Dr. R. Revathi, Dr. Bosco Paul Alapatt, Dr. Felix. M. Philip (2021) A FRAMEWORK FOR THYROID DISEASE PREDICTION SYSTEM USING MACHINE LEARNING TECHNIQUES. Journal of Cardiovascular Disease Research, 12 (5), 1057-1065. doi:10.31838/jcdr.2021.12.05.139





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