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A hybrid optimization algorithm-based feature selection for thyroid disease classifier with rough type-2 fuzzy support vector machine
Computer Science and Engineering, Sona College of Technology, Salem, India.
Computer Science and Engineering, Sona College of Technology, Salem, India.
Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia.
Örebro University, School of Science and Technology. (Centre for Applied Autonomous Sensor Systems)ORCID iD: 0000-0003-1827-9698
2022 (English)In: Expert systems (Print), ISSN 0266-4720, E-ISSN 1468-0394, Vol. 39, no 1, article id e12811Article in journal (Refereed) Published
Abstract [en]

Thyroid hormones are essential for all the metabolic and reproductive activities with significance to growth, and neuron development in the human body. The thyroid hormone dysfunction has many ill consequences, affecting the human population; thereby being a global epidemic. It is noticed that every one in 10 persons suffer from different thyroid disorders in India. In recent years, many researchers have implemented various disease predictive models based on Information and Communications Technology (ICT). Increasing the accuracy of disease classification is a critical and challenging task. To increase the accuracy of classification, in this paper, we propose a hybrid optimization algorithm-based feature selection design for thyroid disease classifier with rough type-2 fuzzy support vector machine. This work uses the hybrid optimization algorithm, which combines the firefly algorithm (FA) and butterfly optimization algorithm (BOA) to select the top-n features. The proposed hybrid firefly butterfly optimization-rough type-2 fuzzy support vector machine (HFBO-RT2FSVM) is evaluated with several key metrics such as specificity, accuracy, and sensitivity. We compare our approach with well-known benchmark methods such as improved grey wolf optimization linear support vector machine (IGWO Linear SVM) and mixed-kernel support vector machine (MKSVM) methods. From the experimental evaluations, we justify that our technique improves the accuracy by large thereby precise in identifying the thyroid disease. HFBO-RT2FSVM model attained an accuracy of 99.28%, having specificity and sensitivity of 98 and 99.2%, respectively.

Place, publisher, year, edition, pages
John Wiley & Sons, 2022. Vol. 39, no 1, article id e12811
Keywords [en]
classification, clinical trial, clustering algorithm, feature selection, fuzzy sets, hormone, machine learning, optimization, support vector machines, thyroid disease
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-94590DOI: 10.1111/exsy.12811ISI: 000697875200001Scopus ID: 2-s2.0-85115291711OAI: oai:DiVA.org:oru-94590DiVA, id: diva2:1597110
Available from: 2021-09-24 Created: 2021-09-24 Last updated: 2022-01-27Bibliographically approved

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