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ISSN: 2157-7420

Journal of Health & Medical Informatics
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Research Article

Presenting a Hybrid Method in Order to Predict the 2009 Pandemic Influenza A (H1N1)

Reza Boostani1*, Mojtaba Rismanchi2, Abbas Khosravani1, Lida Rashidi1, Samaneh Kouchaki1, B. Sabayan2 and K. B. Lankarani2

1CSE and IT Department, Faculty of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran

2Health Policy Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

*Corresponding Author:
Reza Boostani
CSE and IT Department
Faculty of Electrical and Computer Engineering
Shiraz University, Shiraz, Iran
Tel: +98-711-6133577
Fax: +98-711-6474605
E-mail: [email protected]

Received date: October 27, 2011; Accepted date: August 12, 2012; Published date: August 16, 2012

Citation: Boostani R, Rismanchi M, Khosravani A, Rashidi L, Kouchaki S, et al. (2012) Presenting a Hybrid Method in Order to Predict the 2009 Pandemic Influenza A (H1N1). J Health Med Inform 3:112. doi: 10.4172/2157-7420.1000112

Copyright: © 2012 Boostani R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

By the emergence and rapid spread of 2009 pandemic influenza A (H1N1) virus through the world, several methods have been developed to predict and prevent this lethal disease. Although many efforts have been made by statistical and traditional intelligent methods to anticipate this disease, but none of them could satisfy the expectations of specialists. This paper aims to present an efficient hybrid method to predict H1N1 with a reliable sensitivity. In this way, three methods including Gaussian mixture model (GMM), neural network (NN), and fuzzy rule-based system (FRBS) have been fused in order to provide an accurate and reliable prediction scheme to anticipate presence of H1N1 influenza. In this study, 230 individuals were participated and their clinical data were collected. The proposed hybrid scheme was implicated and the results showed to be superior to using each of the decision components containing NN, FRBS, and GMM classifiers. The achieved results produced 95.83% sensitivity and 80.95% specificity on unseen data which support the effectiveness of the hybrid method to predict the influenza in its golden time.

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