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Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study

Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect everyone—men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret...

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Detalles Bibliográficos
Autores principales: Islam, Saima Sharleen, Haque, Md. Samiul, Miah, M. Saef Ullah, Sarwar, Talha Bin, Nugraha, Ramdhan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044232/
https://www.ncbi.nlm.nih.gov/pubmed/35494828
http://dx.doi.org/10.7717/peerj-cs.898
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author Islam, Saima Sharleen
Haque, Md. Samiul
Miah, M. Saef Ullah
Sarwar, Talha Bin
Nugraha, Ramdhan
author_facet Islam, Saima Sharleen
Haque, Md. Samiul
Miah, M. Saef Ullah
Sarwar, Talha Bin
Nugraha, Ramdhan
author_sort Islam, Saima Sharleen
collection PubMed
description Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect everyone—men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine’s machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that the ANN Classifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy.
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spelling pubmed-90442322022-04-28 Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study Islam, Saima Sharleen Haque, Md. Samiul Miah, M. Saef Ullah Sarwar, Talha Bin Nugraha, Ramdhan PeerJ Comput Sci Bioinformatics Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect everyone—men, women, children, adolescents, and the elderly. Thyroid disorders are detected by blood tests, which are notoriously difficult to interpret due to the enormous amount of data necessary to forecast results. For this reason, this study compares eleven machine learning algorithms to determine which one produces the best accuracy for predicting thyroid risk accurately. This study utilizes the Sick-euthyroid dataset, acquired from the University of California, Irvine’s machine learning repository, for this purpose. Since the target variable classes in this dataset are mostly one, the accuracy score does not accurately indicate the prediction outcome. Thus, the evaluation metric contains accuracy and recall ratings. Additionally, the F1-score produces a single value that balances the precision and recall when an uneven distribution class exists. Finally, the F1-score is utilized to evaluate the performance of the employed machine learning algorithms as it is one of the most effective output measurements for unbalanced classification problems. The experiment shows that the ANN Classifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy. PeerJ Inc. 2022-03-03 /pmc/articles/PMC9044232/ /pubmed/35494828 http://dx.doi.org/10.7717/peerj-cs.898 Text en ©2022 Islam et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Islam, Saima Sharleen
Haque, Md. Samiul
Miah, M. Saef Ullah
Sarwar, Talha Bin
Nugraha, Ramdhan
Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
title Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
title_full Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
title_fullStr Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
title_full_unstemmed Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
title_short Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
title_sort application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044232/
https://www.ncbi.nlm.nih.gov/pubmed/35494828
http://dx.doi.org/10.7717/peerj-cs.898
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