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Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Aljaaf, AJ and Al-Jumeily, D and Hussain, A and Fergus, P and Dawson, T and Al-Jumaily, M (2015) Predicting the likelihood of heart failure with a multi level risk assessment using decision tree. In: 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE) . pp. 101-106. (3rd International Conference on Technological Advances in Electrical, Electronics and Computer Engineering, TAEECE 2015, 29th April - 1st May 2015, Beirut, Lebanon).

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science
Date Deposited: 29 Jul 2015 09:53
Last Modified: 27 Nov 2015 09:30
DOI or Identification number: 10.1109/TAEECE.2015.7113608
URI: http://researchonline.ljmu.ac.uk/id/eprint/1765

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