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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| Vydáno v: | PeerJ. Computer science Ročník 8; s. e898 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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03.03.2022
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| ISSN: | 2376-5992, 2376-5992 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| ArticleNumber | e898 |
| Audience | Academic |
| Author | Haque, Md. Samiul Miah, M. Saef Ullah Islam, Saima Sharleen Nugraha, Ramdhan Sarwar, Talha Bin |
| Author_xml | – sequence: 1 givenname: Saima Sharleen surname: Islam fullname: Islam, Saima Sharleen organization: Department of Computer Science, Faculty of Science and Technology, American International University - Bangladesh (AIUB), Dhaka, Bangladesh – sequence: 2 givenname: Md. Samiul surname: Haque fullname: Haque, Md. Samiul organization: Department of Computer Science, Faculty of Science and Technology, American International University - Bangladesh (AIUB), Dhaka, Bangladesh – sequence: 3 givenname: M. Saef Ullah surname: Miah fullname: Miah, M. Saef Ullah organization: Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia – sequence: 4 givenname: Talha Bin surname: Sarwar fullname: Sarwar, Talha Bin organization: Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia – sequence: 5 givenname: Ramdhan surname: Nugraha fullname: Nugraha, Ramdhan organization: Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35494828$$D View this record in MEDLINE/PubMed |
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| Copyright | 2022 Islam et al. COPYRIGHT 2022 PeerJ. Ltd. 2022 Islam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), 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. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2022 Islam et al. 2022 Islam et al. |
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| Keywords | Machine Learning Algorithm Machine Learning Classifier Thyroid Risk Prediction Sick-euthyroid Thyroid Disease |
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| Snippet | Thyroid disease is the general concept for a medical problem that prevents one’s thyroid from producing enough hormones. Thyroid disease can affect... Thyroid disease is the general concept for a medical problem that prevents one's thyroid from producing enough hormones. Thyroid disease can affect... |
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| SubjectTerms | Accuracy Algorithms Algorithms and Analysis of Algorithms Artificial Intelligence Back propagation Bioinformatics Blood Children Classification Clustering Comparative studies Data mining Data Mining and Machine Learning Datasets Decision trees Hormones Hyperthyroidism Hypothyroidism Illnesses Machine learning Machine Learning Algorithm Machine Learning Classifier Medical examination Medical research Medicine, Experimental Microbalances Neural networks Performance evaluation Physiology Recall Risk factors Sick-euthyroid Thyroid cancer Thyroid Disease Thyroid diseases Thyroid Risk Prediction |
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| Title | Application of machine learning algorithms to predict the thyroid disease risk: an experimental comparative study |
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