Effective diagnosis of heart disease through neural networks ensembles |
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| Authors: | Resul Das Ibrahim Turkoglu Abdulkadir Sengur |
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| Affiliation: | 1. Department of Computer Engineering, Sharif University of Technology, Azadi Ave, Tehran, Iran;2. Department of Computer Science and Engineering, University of Washington, Seattle, United States;3. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia;4. Department of Electrical and Computer Engineering, Isfahan University of Technolgy, Isfahan, Iran;5. Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran;6. Professor of cardiology, Isfahan Cardiovascular Research Center, Isfahan University of Medical Sciences, Isfahan, Iran;1. Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria 3217, Australia;2. Department of Computer Science and Engineering, University of Washington, Seattle, United States;3. Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran;4. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran;5. Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences,Isfahan,Iran & Faculty of Medicine, SPPH, University of British Columbia, Vancouver,BC, Canada;1. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen, China;2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China;3. School of Computing Science and Engineering, VIT University, Vellore 632014, Tamil Nadu, India. |
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| 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. |
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| Keywords: | Heart disease SAS base software Neural networks Ensemble based model |
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