Open AccessA comparative study of support vector machine and logistic regression for the diagnosis of thyroid dysfunction
Author(s) -
Deepthi Gurram,
M. R. Narasinga Rao
Publication year - 2017
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i1.1.9714
Subject(s) - logistic regression , thyroid , thyroid dysfunction , support vector machine , hormone , medicine , thyroid disease , disease , computer science , artificial intelligence
Thyroid is one of the vital diseases that influence individuals of any age group now a day. Infections of the thyroid, incorporate conditions 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.