A Role of decision Tree classification data Mining Technique in Diagnosing Thyroid disease

Abstract

Thyroid disease is one of the common diseases to be found in human beings. The disease of thyroid gland varies from the low production as well as high production of the thyroid hormone, respectively. However, it is always recommended to diagnose the disease at an earlier stage in order to prevent further harmful effects and to provide the treatment to keep the thyroid hormone at normal level. Data Mining is playing vital role in health care applications. It is used to analyze the large volumes of data. One of the important task in data mining is predicting disease in earlier stage, which assist physician to give better treatment to the patients. Classification is one of the most significant data mining technique. It is supervised learning and used to classify predefined data sets. Data mining technique is mainly used in healthcare organizations for decision making, diagnosing diseases and giving better treatment to the patients. The data set used for this study on hypothyroid is taken from University of California Irvine (UCI) data repository. The entire research work is to be carried out with Waikato Environment in Knowledge Analysis (WEKA) open source software under Windows 7 environment. An experimental study is to be carried out using data mining techniques such as J48 and Decision stump tree. The data records are classified as negative, compensated, primary and secondary hypothyroid. As a result, the performance will be evaluated for both classification techniques and their accuracy will be compared through confusion matrix. It has been concluded that J48 gives better accuracy than the decision stump tree technique.