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Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

S. J. K. Jagadeesh Kumar1, P. Parthasarathi2, Mehedi Masud3, Jehad F. Al-Amri4, Mohamed Abouhawwash5,6,*

1 Department of Computer Science and Engineering, Kathir College of Engineering, Coimbatore, 641062, India
2 Department of Computer science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, 638401, Tamilnadu, India
3 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif, 21944, Saudi Arabia
5 Department of Mathematics, Faculty of Science, Mansoura University, Mansoura 35516, Egypt
6 Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, East Lansing, MI, 48824, USA

* Corresponding Author: Mohamed Abouhawwash. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 2909-2924.


The main task of thyroid hormones is controlling the metabolism rate of humans, the development of neurons, and the significant growth of reproductive activities. In medical science, thyroid disorder will lead to creating thyroiditis and thyroid cancer. The two main thyroid disorders are hyperthyroidism and hypothyroidism. Many research works focus on the prediction of thyroid disorder. To improve the accuracy in the classification of thyroid disorder this paper proposes optimization-based feature selection by using differential evolution with the Butterfly optimization algorithm (DE-BOA). For the classifier fuzzy C-means algorithm (FCM) is used. The proposed DEBOA-FCM is evaluated with parametric metric measures of sensitivity, specificity, and accuracy. In this work, the thyroid disease dataset collected from the machine learning University of California Irvine (UCI) database was used. The accuracy rate for the Differential Evolutionary algorithm got 0.884, the Butterfly optimization algorithm got 0.906, Fuzzy C-Means algorithm got 0.899 and DEBOA + Focused Concept Miner (FCM) proposed work 0.943.


Cite This Article

S. J. K. J. Kumar, P. Parthasarathi, M. Masud, J. F. Al-Amri and M. Abouhawwash, "Butterfly optimized feature selection with fuzzy c-means classifier for thyroid prediction," Intelligent Automation & Soft Computing, vol. 35, no.3, pp. 2909–2924, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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