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

Autor:	Islam SS; Department of Computer Science, Faculty of Science and Technology, American International University - Bangladesh (AIUB), Dhaka, Bangladesh., Haque MS; Department of Computer Science, Faculty of Science and Technology, American International University - Bangladesh (AIUB), Dhaka, Bangladesh., Miah MSU; Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia., Sarwar TB; Faculty of Computing, College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia., Nugraha R; Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia.
Jazyk:	angličtina
Zdroj:	PeerJ. Computer science [PeerJ Comput Sci] 2022 Mar 03; Vol. 8, pp. e898. Date of Electronic Publication: 2022 Mar 03 (Print Publication: 2022).
DOI:	10.7717/peerj-cs.898
Abstrakt:	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. Competing Interests: The authors declare there are no competing interests. (©2022 Islam et al.)
Databáze:	MEDLINE
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