Title
Thyroid Disease Detection using SVM Algorithm
Abstract
Thyroid disease is clinical disease of high prevalence in the world of population leading cause of medical diagnosis and prediction development which medical science is complicated axiom. One of most used treatments is sodium levothyroxine, also known as LT4, a synthesis thyroid hormone used in the treatment of thyroid disorders and diseases. Prediction about the treatment can be important for supporting endocrinologists’ activities and improve the quality of the patients’ life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of hormonal parameters of people. A person can develop problems if their thyroid overproduces or underproduces hormones. These states are known as hyperthyroidism and hypothyroidism. The proposed system predicts the thyroid treatment using different Machine Learning algorithms. In this paper our objective is to predict thyroid disease to aware the patient. Thyroid is predicted in three parameters that is Normal, hypothyroidism, hyperthyroidism In Machine learning various algorithm are used to predict the thyroid disease. SVM is also used to predict the thyroid. In this paper we proposed SVM algorithm that gives the maximum accuracy as compared to other algorithms. SVM gives 95.98 percent of accuracy. The original dataset contains unwanted data with null csv so the new dataset has been created which removes the unwanted data. SVM algorithm which gives maximum accuracy that is used to build the model. Then proposed system uses thyroid disease dataset. We need 30 percent data for testing and 70 percent data is been used for training for training.
Key Words
Thyroid disease is clinical disease of high prevalence in the world of population leading cause of medical diagnosis and prediction development which medical science is complicated axiom. One of most used treatments is sodium levothyroxine, also known as LT4, a synthesis thyroid hormone used in the treatment of thyroid disorders and diseases. Prediction about the treatment can be important for supporting endocrinologists’ activities and improve the quality of the patients’ life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of hormonal parameters of people. A person can develop problems if their thyroid overproduces or underproduces hormones. These states are known as hyperthyroidism and hypothyroidism. The proposed system predicts the thyroid treatment using different Machine Learning algorithms. In this paper our objective is to predict thyroid disease to aware the patient. Thyroid is predicted in three parameters that is Normal, hypothyroidism, hyperthyroidism In Machine learning various algorithm are used to predict the thyroid disease. SVM is also used to predict the thyroid. In this paper we proposed SVM algorithm that gives the maximum accuracy as compared to other algorithms. SVM gives 95.98 percent of accuracy. The original dataset contains unwanted data with null csv so the new dataset has been created which removes the unwanted data. SVM algorithm which gives maximum accuracy that is used to build the model. Then proposed system uses thyroid disease dataset. We need 30 percent data for testing and 70 percent data is been used for training for training.
Cite This Article
"Thyroid Disease Detection using SVM Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.141-145, May-2023, Available :
http://www.jetir.org/papers/JETIRFX06022.pdf
ISSN
2349-5162 | Impact Factor 7.95 Calculate by Google Scholar
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Cite This Article
"Thyroid Disease Detection using SVM Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp141-145, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06022.pdf
Publication Details
Published Paper ID: JETIRFX06022
Registration ID: 517011
Published In: Volume 10 | Issue 5 | Year May-2023
DOI (Digital Object Identifier):
Page No: 141-145
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
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