Paper Info
Title
Effective diagnosis of heart disease through neural networks ensembles
Abstract
In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.
Year
DOI
Venue
2009
10.1016/j.eswa.2008.09.013
Expert Syst. Appl.
Keywords
Field
DocType
heart disease,sas base software,new tool,neural networks,new model,new methodology,effective model,cleveland heart disease database,proposed system,effective medical decision support,heart disease diagnosis,neural networks ensemble,proposed tool,effective diagnosis,ensemble based model,posterior probability
Data mining,Computer science,Decision support system,Posterior probability,Software,Artificial intelligence,Artificial neural network,Machine learning,Heart disease
Journal
Volume
Issue
ISSN
36
4
Expert Systems With Applications
Citations 
PageRank 
References 
84
3.02
4
Authors
3
Name
Order
Citations
PageRank
Resul Das123711.38
İbrahim Türkoğlu247433.96
Abdulkadir Sengur349938.21