Abstract
Hypothyroidism is a condition when the thyroid gland produces less hormone than a normal range. As the symptoms of hypothyroidism are not clear, in past days many researchers have tried to work on fine-tuning hypothyroidism detection techniques. In this work, we have proposed a multiclass hypothyroidism detection technique using machine learning methods. Exploratory data analysis-based feature selection has proven helpful in eliminating irrelevant features for classification. Different classifiers have been experimented and compared to find the best-fitted classifier among the experimented ones for multiclass hypothyroidism classification. Experimental results on publicly available dataset shows the higher efficacy of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rakov, H., et al.: Sex-specific phenotypes of hyperthyroidism and hypothyroidism in mice. Biol. Sex Differ. 7(1), 1–13 (2016)
Samuel, E.J.V., et al.: Increasing prevalence of osteoporosis, hypothyroidism and endogenous estrogen with germ cells. Open J. Rheumatol. Autoimmun. Dis. 4(3), 131–137 (2014)
Lorini, R., et al.: Hashimoto’s thyroiditis. Pediatric endocrinology reviews: PER, vol. 1, pp. 205–211 (2003); discussion 211
Allahabadia, A., et al.: Diagnosis and treatment of primary hypothyroidism. Br. Med. J. 338, b725 (2009)
Kostoglou-Athanassiou, I., Ntalles, K.: Hypothyroidism-new aspects of an old disease. Hippokratia 14(2), 82 (2010)
Benediktsson, R., Toft, A.D.: Management of the unexpected result: compensated hypothyroidism. Postgrad. Med. J. 74(878), 729–732 (1998)
Chen, L., et al.: Mining health examination records—a graph- based approach. IEEE Trans. Knowl. Data Eng. 28(9), 2423–2437 (2016)
Ammulu, K., Venugopal, T.: Thyroid data prediction using data classification algorithm. Int. J. Innov. Res. Sci. Technol 4(2), 208–212 (2017)
Shankar, K., et al.: Optimal feature- based multi-kernel SVM approach for thyroid disease classification. J. Supercomput. 76(2), 1128–1143 (2020)
Ioniţă, I., Ioniţă, L.: Prediction of thyroid disease using data mining techniques. BRAIN. Broad Res. Artif. Intell. Neurosci. 7(3), 115–124 (2016)
Ahmed, J., Soomrani, M.A.R.: TDTD: thyroid disease type diagnostics. In: 2016 International Conference on Intelligent Systems Engineering (ICISE). IEEE (2016)
Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160(1), 3–24 (2007)
Deng, Z., et al.: Efficient kNN classification algorithm for big data. Neurocomputing 195, 143–148 (2016)
Mavroforakis, M.E., Theodoridis, S.: A geometric approach to support vector machine (SVM) classification. IEEE Trans. Neural Netw. 17(3), 671–682 (2006)
Kirar, J.S., Agrawal, R.K.: Composite kernel support vector machine based performance enhancement of brain computer interface in conjunction with spatial filter. Biomed. Signal Process. Control 33, 151–160 (2017)
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. in icml. Citeseer (1999)
Liaw, A., Wiener, M.: Classification and regression by randomForest. R News 2(3), 18–22 (2002)
Banu, G.: Predicting thyroid disease using linear discriminant analysis (LDA) data mining technique. Commun. Appl. Electron. 4, 4–6 (2016). https://doi.org/10.5120/cae2016651990
Tandan, S.R.: Diagnosis and classification of hypothyroid disease using data mining techniques. IJERT 6 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Das, R., Saraswat, S., Chandel, D., Karan, S., Kirar, J.S. (2022). An AI Driven Approach for Multiclass Hypothyroidism Classification. In: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (eds) Advanced Network Technologies and Intelligent Computing. ANTIC 2021. Communications in Computer and Information Science, vol 1534. Springer, Cham. https://doi.org/10.1007/978-3-030-96040-7_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-96040-7_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96039-1
Online ISBN: 978-3-030-96040-7
eBook Packages: Computer ScienceComputer Science (R0)