A ROLE OF DECISION TREE CLASSIFICATION DATA MINING TECHNIQUES TO PREDICT CHRONIC KIDNEY DISEASE

Abstract

Chronic Kidney Disease is a condition in which the kidneys are damaged and cannot filter blood as well as they should. Because of this, excess fluid and waste from blood remain in the body and may cause other health problems, such as heart disease and stroke. 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 tasks 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 techniques. 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 chronic kidney disease 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, Decision stump, Random Forest tree, REP tree and Random tree. As a result, the performance will be evaluated for classification techniques and their accuracy will be compared through confusion matrix. It has been concluded that the Random Forest tree  gives better accuracy than other classification techniques.