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Diagnosing tuberculosis with a novel support vector machine-based artificial immune recognition system

Saybani MR 1 , Shamshirband S 2 , Golzari Hormozi S 3 , Wah TY 1 , Aghabozorgi S 1 , Pourhoseingholi MA 4 Show all authors , Olariu T 5

Affiliations 

  • 1 Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, Kula Lumpur, Malaysia
  • 2 Department of Computer Science, Chalous Branch, Islamic Azad University, Chalous, IR Iran
  • 3 Department of Electrical and Computer Engineering, Faculty of Engineering, University of Hormozgan, Bandar Abbas, IR Iran
  • 4 Gastroenterology and Liver Diseases Research Center, Shahid Beheshti University of Medical Sciences, Tehran, IR Iran
  • 5 Department of Intensive Care, Faculty of Medicine, Western University of Arad, Arad, Romania
Iran Red Crescent Med J, 2015 Apr;17(4):e24557.
PMID: 26023340 DOI: 10.5812/ircmj.17(4)2015.24557

Abstract

Tuberculosis (TB) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. Diagnosis based on cultured specimens is the reference standard, however results take weeks to process. Scientists are looking for early detection strategies, which remain the cornerstone of tuberculosis control. Consequently there is a need to develop an expert system that helps medical professionals to accurately and quickly diagnose the disease. Artificial Immune Recognition System (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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