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 inte...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Peerj Inc.
2022
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45441/ |
| _version_ | 1848827419273723904 |
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| author | Islam, Saima Sharleen Haque, Md Samiul Miah, M. Saef Ullah Sarwar, Talha Nugraha, Ramdhan |
| author_facet | Islam, Saima Sharleen Haque, Md Samiul Miah, M. Saef Ullah Sarwar, Talha Nugraha, Ramdhan |
| author_sort | Islam, Saima Sharleen |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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 theANNClassifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy. |
| first_indexed | 2025-11-15T04:00:25Z |
| format | Article |
| id | ump-45441 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:00:25Z |
| publishDate | 2022 |
| publisher | Peerj Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-454412025-08-20T04:26:26Z https://umpir.ump.edu.my/id/eprint/45441/ 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 Nugraha, Ramdhan QA75 Electronic computers. Computer science RC Internal medicine 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 theANNClassifier with an F1-score of 0.957 outperforms the other nine algorithms in terms of accuracy. Peerj Inc. 2022-03 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/45441/1/Application%20of%20machine%20learning%20algorithms%20to%20predict%20the%20thyroid.pdf Islam, Saima Sharleen and Haque, Md Samiul and Miah, M. Saef Ullah and Sarwar, Talha and Nugraha, Ramdhan (2022) Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study. PeerJ Computer Science, 8 (e898). pp. 1-35. ISSN 2376-5992. (Published) https://doi.org/10.7717/peerj-cs.898 https://doi.org/10.7717/peerj-cs.898 https://doi.org/10.7717/peerj-cs.898 |
| spellingShingle | QA75 Electronic computers. Computer science RC Internal medicine Islam, Saima Sharleen Haque, Md Samiul Miah, M. Saef Ullah Sarwar, Talha 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 | QA75 Electronic computers. Computer science RC Internal medicine |
| url | https://umpir.ump.edu.my/id/eprint/45441/ https://umpir.ump.edu.my/id/eprint/45441/ https://umpir.ump.edu.my/id/eprint/45441/ |