Heart Disease Prediction Using Machine Learning and Data Mining Techniques

Abstract

Heart disease is that the main reason for death within the world over the last decade. Researchers are victimisation many data mining techniques to assist health care professionals in the diagnosing of Heart disease. However 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 e cient detection technique. Decision Tree is one in every of the e ective data processing ways used. This research compares di erent algorithms of Decision Tree classi cation seeking better performance in heart disease diagnosis using R studio. The algorithms which are tested is J48 algorithm,Logistic Model Tree algorithm, Random Forest algorithm,Support vector machine,and K Nearest neighbour. The existing datasets of heart disease patients from Cleveland database of UCI repository is used to take a look at and justify the performance of call tree algorithms.This dataset consists of 618 instances and 76 attributes. Subsequently, the classi cation rule that has optimum potential are advised to be used in sizeable information. 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.