Predicting Presence of Heart Disease in Patients using Decision Tree Classification Techniques

 Predicting Presence of Heart Disease in Patients using Decision Tree Classification Techniques

Abstract – Heart disease is the leading cause of death in the world over the past 10 years. Almost one person dies of heart disease about every minute in the United States alone. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. However using data mining technique can reduce the number of test that are required.  In order to reduce number of deaths from heart diseases there have to be a quick and efficient detection technique. Decision Tree is one of the successful data mining techniques used. This research compares different algorithms of Decision Tree classification seeking better performance in heart disease diagnosis using WEKA. The algorithms which are tested is J48 algorithm, Logistic model tree algorithm and Random Forest algorithm. The existing datasets of heart disease patients from Cleveland database of UCI repository is used to test and justify the performance of decision tree algorithms. This datasets consists of 303 instances and 76 attributes. Subsequently, the classification algorithm that has optimal potential will be suggested for use in sizeable data. The goal of this study is to extract hidden patterns by applying data mining techniques, which are noteworthy to heart diseases and to predict the presence of heart disease in patients where this presence is valued from “no presence” to “likely presence”.

Author : ss infotech

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