An Experimental Study of Applying Machine Learning in Prediction of Thyroid Disease
DOI:
https://doi.org/10.26438/ijcse/v7i1.130133Keywords:
Machine Learning, Health Care, Thyroid Disease, PredictionAbstract
New advancements have made it workable for an extensive variety of individuals – including humanities and sociology scholastics, advertisers, legislative associations, instructive foundations – to deliver, share, collaborate and arrange data. Monstrous informational collections that were once dark and particular are being amassed and made effectively open. The Huge volumes of heterogeneous therapeutic information these days expanding and easily obtainable from various healthcare organizations. Nowadays, the Thyroid disease is one of the common diseases found in human. The Thyroid hormones created by the thyroid organ to help the control of the body's digestion. Because of the variations from the norm of thyroid capacity, there might be a lower production of thyroid hormone, which is known as hypothyroidism, or higher production of thyroid hormone, which is known as hyperthyroidism. In this paper, an examination of thyroid disease is carried out by performing experiment of various Machine Learning algorithms techniques such as Naïve Bayes, Support Vector Machine, Multiclass Classifier, Logistic and K Nearest Neighbour. The informational index utilized for this investigation on hypothyroid is taken from UCI information store. The experiment is also completed with WEKA and RConsole. The comparison of various parameters are done and as a result the execution and investigation of different grouping calculation is determined. In the result, it is found that Multiclass Classifier gives preferable exactness over other embraced calculations.
References
[1] I. Mandal and N. Sairam, “Accurate prediction of coronary artery disease using reliable diagnosis system,” J. Med. Syst., vol. 36, no. 5, pp. 3353–3373, 2012.
[2] J. Sun and C. K. Reddy, “Big data analytics for healthcare,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’13, 2013.
[3] C. L. Philip Chen and C. Y. Zhang, “Data-intensive applications, challenges, techniques and technologies: A survey on Big Data,” Inf. Sci. (Ny)., vol. 275, pp. 314–347, 2014.
[4] J. A. Cruz and D. S. Wishart, “Applications of machine learning in cancer prediction and prognosis,” Cancer Informatics. 2006.
[5] F. Amato, A. López, E. M. Peña-Méndez, P. Vaňhara, A. Hampl, and J. Havel, “Artificial neural networks in medical diagnosis,” Journal of Applied Biomedicine. 2013.
[6] R. Palaniappan, K. Sundaraj, and S. Sundaraj, “A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals,” BMC Bioinformatics, 2014.
[7] L. Tapak, H. Mahjub, O. Hamidi, and J. Poorolajal, “Real-data comparison of data mining methods in prediction of diabetes in Iran,” Healthc. Inform. Res., 2013.
[8] D. Delen, “Analysis of cancer data: A data mining approach,” Expert Syst., 2009.
[9] A. Agrawal, S. Misra, R. Narayanan, L. Polepeddi, and A. Choudhary, “A lung cancer outcome calculator using ensemble data mining on SEER data,” in Proceedings of the Tenth International Workshop on Data Mining in Bioinformatics - BIOKDD ’11, 2011.
[10] T. Nguyen, A. Khosravi, D. Creighton, and S. Nahavandi, “Classification of healthcare data using genetic fuzzy logic system and wavelets,” Expert Syst. Appl., 2015.
[11] T. C. Chen and T. C. Hsu, “A GAs based approach for mining breast cancer pattern,” Expert Syst. Appl., 2006.
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