Multidrug-resistant tuberculosis risk factors assessment with intuitionistic fuzzy cognitive maps

DOĞU E., ALBAYRAK Y. E., Tuncay E.

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, vol.38, no.1, pp.1083-1095, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 1
  • Publication Date: 2020
  • Doi Number: 10.3233/jifs-179470
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1083-1095
  • Keywords: Multidrug-resistant tuberculosis, intuitionistic fuzzy cognitive maps, medical decision support, risk factors, DECISION-MAKING, SUPPORT-SYSTEM, DIAGNOSIS, MANAGEMENT, DEFAULT
  • Galatasaray University Affiliated: Yes


Tuberculosis (TB) bacteria may develop resistance to the drugs, which are used in TB treatment. Multidrug-resistant TB (MDR-TB) is a type of TB that does not respond to at least rifampicin and isoniazid, the 2 most powerful anti-TB drugs. MDR-TB requires a more compelling treatment and it is more difficult to diagnose. The experience of physician is the key factor in the success of MDR-TB diagnose. The existence of TB bacteria in the body can be observed relatively faster with a standard sputum smear however, drug-susceptibility tests require nearly 45 days. To cope with this infectious disease, it is vital to estimate the resistance in a newly diagnosed TB patient to plan the initialization of the treatment in the testing period. Herein, the purpose of this study is to build a framework and establish a mathematical model that will help decision makers (physicians) while estimating the risk of multidrug resistance when a new tuberculosis patient arrives, using intuitionistic fuzzy cognitive maps (IFCM). Intuitionistic fuzzy sets are utilized to reflect the decision makers' hesitancy degrees in the model.