An Experimental Study of Applying Machine Learning in Prediction of Thyroid Disease - Patel, Hetal (International Journal of Computer Sciences and Engineering, 2019)
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.