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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM19030

A Literature Review on Thyroid Detection using Machine Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM19030

Volume:

7 |

5 |

May 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

PMS

Prof. Mohammad Sajid

IP

Isha Patil

MB

Mayuri Balapure

SG

Sonali Gadekar

ST

Samiksha Tembhurne

RB

Riya Bawne

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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. 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. Machine Learning plays a very deciding role in the disease prediction. Machine Learning algorithms, SVM – support vector machine, KNN – K-nearest neighbours, Random Forest , K- Means Algorithms are used to predict the patient’s risk of getting thyroid disease. Web app is created to get data from users to predict the type of disease.

KEYWORDS

Hyperthyroidism Hypothyroidism SVM KNN K-Means.

Cite This Article

Prof. Mohammad Sajid, Isha Patil, Mayuri Balapure, Sonali Gadekar, Samiksha Tembhurne, Riya Bawne, "A Literature Review on Thyroid Detection using Machine Learning," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 5, May 2023. https://doi.org/10.55041/IJSREM19030

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Publication Date

5/4/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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