title: Tuberculosis disease diagnosis using artificial immune recognition system.
abstract: BACKGROUND
  There is a high risk of tuberculosis (TB) disease diagnosis among conventional methods.
  
  
  OBJECTIVE
  This study is aimed at diagnosing TB using hybrid machine learning approaches.
  
  
  METHODS
  Patient epicrisis reports obtained from the Pasteur Laboratory in the north of Iran were used. All 175 samples have twenty features. The features are classified based on incorporating a fuzzy logic controller and artificial immune recognition system. The features are normalized through a fuzzy rule based on a labeling system. The labeled features are categorized into normal and tuberculosis classes using the Artificial Immune Recognition Algorithm.
  
  
  RESULTS
  Overall, the highest classification accuracy reached was for the 0.8 learning rate (α) values. The artificial immune recognition system (AIRS) classification approaches using fuzzy logic also yielded better diagnosis results in terms of detection accuracy compared to other empirical methods. Classification accuracy was 99.14%, sensitivity 87.00%, and specificity 86.12%.
authors.affiliation: 1. Department of Computer Science, Chalous Branch, Islamic Azad University (IAU), 46615-397 Chalous, Mazandaran, Iran;
authors.forename: Shahaboddin
authors.initials: S
authors.lastname: Shamshirband
authors.affiliation: 2. Department of Health Services Administration, Science and Research Branch, Islamic Azad University, Shiraz Fars, Iran;
authors.forename: Somayeh
authors.initials: S
authors.lastname: Hessam
authors.affiliation: 3. Department of Computer Engineering, University of Guilan, Iran.
authors.forename: Hossein
authors.initials: H
authors.lastname: Javidnia
authors.affiliation: 4. Faculty of Science and Technology, Bournemouth University, UK.
authors.forename: Mohsen
authors.initials: M
authors.lastname: Amiribesheli
authors.affiliation: 2. Department of Health Services Administration, Science and Research Branch, Islamic Azad University, Shiraz Fars, Iran;
authors.forename: Shaghayegh
authors.initials: S
authors.lastname: Vahdat
authors.affiliation: 5. University of Niš, Faculty of Mechanical Engineering, Department for Mechatronics and Control, Aleksandra Medvedeva 14, 18000 Niš, Serbia.
authors.forename: Dalibor
authors.initials: D
authors.lastname: Petković
authors.affiliation: 6. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Abdullah
authors.initials: A
authors.lastname: Gani
authors.affiliation: 6. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia.
authors.forename: Miss Laiha Mat
authors.initials: ML
authors.lastname: Kiah
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017143:Fuzzy Logic; D006801:Humans; D007107:Immune System; D014376:Tuberculosis / Q000175:diagnosis* / Q000276:immunology
keywords: Artificial Immune Recognition System; Fuzzy system; Safety.; Tuberculosis