A comparison of classification methods on diagnosis of thyroid diseases

Abstract

Thyroid gland is one of the endocrine glands in the human body which produces thyroid hormone. This gland actively produces two kinds of hormone, namely thyroxine (T4) and triiodothyronine (T3). These hormones aim to produce protein, govern body metabolism, as well as to control body temperature circulation. Either excess or lack of these hormones will disturb those activities. The condition of excessive hormones is called hyperthyroid while the condition of lacking hormones is called hypothyroid. The major factor that influences the volume of the produced T3 and T4 hormones is iodine, because it is the main building-block substance of those hormones. The imbalance condition of this substance prevents thyroid to work properly. To identify the type of thyroid (normal, hypothyroid, hyperthyroid), WEKA (Waikato Environment for Knowledge Analysis) machine learning software is utilized. The thyroid dataset is taken from UCI (University of California - Irvine) machine learning repository as many as 215 instances. The test result shows that among six different methods available in WEKA, MLP (Multilayer Perceptron) method gives result with the highest accuracy, up to 96.74%, while BPA (Back Propagation Algorithm) methods produces result with the lowest accuracy, of 69.77%.