Abstract:
Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent stu...Show MoreMetadata
Abstract:
Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy.
Published in: 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)
Date of Conference: 29 April 2015 - 01 May 2015
Date Added to IEEE Xplore: 01 June 2015
ISBN Information: