Copyright © 2018 Deepthi Gurram, M. R. Narasinga Rao. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. International Journal of Engineering & Technology, 7 (1.1) (2018) 326-328 International Journal of Engineering & Technology Website: www.sciencepubco.com/index.php/IJET Research paper A comparative study of support vector machine and logistic regression for the diagnosis of thyroid dysfunction Deepthi Gurram 1 *, M. R. Narasinga Rao 2 1 Research Scholar, Department of Computer Science, K L University, Vaddeswaram, Andhra Pradesh, India 2 Department of Computer Science & Engineering , K L University, Vaddeswaram, Andhra Pradesh, India *Corresponding author E-mail: deepug2110@gmail.com Abstract Thyroid is one of the vital diseases that influence individuals of any age group now a day. Infections of the thyroid, incorporate condi- tions related with extreme release of thyroid hormones (Hyper thyroidism) which is likewise called thyrotoxicosis and those related with thyroid hormone insufficiency (Hypothyroidism). Expectation of these two sorts of thyroid disease is critical for thyroid analysis. In this paper, support vector machines and logistic regression are proposed for predicting patients with thyrotoxicosis and without thyrotoxicosis. The outcomes demonstrate that, logistic regression perform well over support vector machine with 98.92% exactness. Keywords: Logistic Regression; Precision; Recall; Support Vector Machine; Thyrotoxicosis. 1. Introduction These days, by the rapid development of innovation and infor- mation in medical sciences, the software engineering experts are fit for providing expert frameworks to determine various types of diseases with high exactness. The therapeutic experts are made to utilize these frameworks because of the existence of some prob- lems at the time of prediction process [1]. Disease diagnosis oper- ation utilizing expert frameworks are performed in light of a set of disease manifestations. These frameworks depend on machine learning procedures which help the doctor and a physician to limit the expenses and time and can act as an expert advisor in making successful conclusions. In the human body, the thyroid organ is an essential organ. It pro- duces thyroid hormones to keep up our body metabolism [2]. The thyroid organ is situated in the front of the neck and underneath the Adam's apple. The thyroid produces two noteworthy hormones called T3 (triiodothyronine) and T4 (thyroxine). These T3 and T4 hormones make a trip in our blood to all parts of our body and influence practically every cell in the body, and controls our body's function. On the off chance that, the measure of thyroid hormone diminishes in our blood and our body work gets back off, this condition is called hypothyroidism [3]. The signs of hypothy- roid are depression, exhaustion of body strength, tiredness, consti- pation, excess weight, cramps, dry skin, sexual disorders and in- fertility. On the off chance that, the increased measure of thyroid hormones found in our blood, our body functions will increase. This condition is called hyperthyroidism. The indications of hy- perthyroid are anxiety, palpitation, exhausted body quality, trem- ors, loss of weight, diarrhoea, menstrual disorder and exophthal- mia. Specialists can fuse various factors, including clinical as- sessment, blood tests, imaging tests, biopsies, and different tests to analyze thyroid illness. A typical utilized strategy is a test, called the thyroid- stimulating hormone (TSH) test, which can recognize thyroid issue even before the onset of indications. These days, CAD frameworks are getting increasingly prominent. With the assistance of the CAD frameworks, the identified errors a doctor can make, over the span of analysis can be kept away from, and the medical information can be inspected in shorter time and more definite as well [4]. Machine learning procedures are pro- gressively acquainted with to build the CAD frameworks inferable from its firm capacity of draw out complex relationships in the biomedical information. As of late, different strategies have been proposed to take care of this issue. 2. Related work In 2014, Baydaa S. B. Alyas [5] planned an inventive framework which would diagnose the thyroid patients with least execution time and superior. This system should enable the healthcare ex- perts to answer inquiries that describe the endocrine organ dys- function and that allow them to take any clinical conclusions. In 2013, Ahmad Taher Azar et al. [6] proposed a correlation amongst hard and fuzzy clustering algorithms for thyroid maladies informational index in order to locate the optimal number of clus- ters. Distinctive scalar validity measures are utilized in looking at the overall execution of the proposed clustering frameworks. To locate the optimal number of clusters, elbow paradigm is enforced. The clustering outcomes for all algorithms are then imagined by the Sammon mapping strategy to locate a low-dimensional (ordi- narily 2D or 3D) portrayal of a collection of points scattered in a high dimensional pattern space. In 2013, Ms. Wrushali Mendre Dr. Ranjana D. Raut [7] explored the potentiality of neural network to segregate the two subtypes, hypothyroid and negative form of thyroid issue based on the ar- gument of laboratory medical information base. The best parame- ters are distinguished for the neural networks like Multilayer Per- ceptron (MLP), Radial Basis Function (RBF) and Principal Com- ponent Analysis (PCA). In 2012, Hui-Ling Chen, et al. [8] represented a three-phase expert framework based on a hybrid support vector machines (SVM)