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ISSN: 2157-7420

Journal of Health & Medical Informatics
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  • Research Article   
  • J Health Med Informat 2010, Vol 1(1): 103
  • DOI: 10.4172/2157-7420.1000103

Application of Artificial Intelligence in the Diagnosis of Eosinophilia

S. Martha Merlyn, S. Shiney Valentina, Sachidanand Singh, J. Jannet Vennila and Atul Kumar*
Department of Bioinformatics, Karunya University, Coimbatore, Tamil Nadu, India
*Corresponding Author : Atul Kumar, Department of Bioinformatics, Karunya University, Coimbatore, Tamil Nadu, India, Tel: +919488523540, Email: [email protected]

Received Date: Dec 13, 2010 / Accepted Date: Dec 27, 2010 / Published Date: Dec 29, 2010

Abstract

Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. Fuzzy logic controller (FLC) was used to design a system for the diagnosis of eosinophilia. Eosinophilia is a common disease which is prevalent in people. High eosinophilic count in blood is an evidence for prevalence of eosinophilia. It is caused by both external and internal factors. The remedial measures for eosinophilia should be taken at the earliest, as the effect for it can vary from mild to severe. The design is based on Mamdani-style inference system which is very good for the representation of human reasoning and effective analysis. The implementation is done using MATLAB fuzzy logic tools. The effectiveness of FLC depends on the rules formed and interpretation of surface data. The performance of our FLC was predicted about 82.5% and a minimum error was obtained as 10.0%.

Citation: Merlyn SM, Valentina SS, Singh S, Kumar A (2010) Application of Artificial Intelligence in the Diagnosis of Eosinophilia. J Health Med Informat 1:103. Doi: 10.4172/2157-7420.1000103

Copyright: © 2010 Merlyn SM, 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.

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