Thyroid Disease Classification Using Decision Tree and SVM
K. Dharmarajan
Abstract
Thyroid is one of the disease that can be increasing day by day due to their lifestyle. Thyroid disease is avery common disease among humans. A Thyroid disorders are the conditions that affects the thyroid glandand also the butterfly-shaped gland at the front of the neck. The thyroid gland is located on the below ofAdam’s apple that wrapped around the trachea. The Hydroxide is also known as T4 and it is the primaryhormone produced by the gland. Thyroid hormone that regulates the body numerous metabolic mechanismsthroughout the body. When compare to male, female is more affected than male due to the thyroid disease.In thyroid, there are two types of diseases, They are Hyperthyroidism and Hypothyroidism. Hypothyroidismthat produces a lots of thyroid hormone in the blood and in Hypothyroidism that produces less thyroidhormone in the blood. This is controlled by the pituitary gland and hypothalamus. The disorders of thesetissues can also be affecting a thyroid function and it causes the thyroid problems. There are the Specifictypes of thyroid glands are includes: Hypothyroidism, Hyperthyroidism, Goiter, Thyroid nodules andThyroid cancer. This paper describes about the diagnosis of thyroid disorders using decision tree attributesplitting rules. The proposed method, classifies the thyroid nodules accurately and efficiently. In this study,the comparative thyroid disease diagnosis were performed by using the Machine learning techniques thatcan be a method which is Support Vector Machine (SVM), Naïve Bayes and Decision Trees. The accuracyof this classification is to be 99.89%. This result is very efficient when compared to our previous work thatused the Decision tree.