An expert system to diagnose pneumonia using fuzzy logic

Arani, L.A. and Sadoughi, F. and Langarizadeh, M. (2019) An expert system to diagnose pneumonia using fuzzy logic. Acta Informatica Medica, 27 (2). pp. 103-107.

[img] Text

Download (1MB)
Official URL:


Introduction: Pneumonia is the most common and widespread killing disease of respiratory system which is difficult to diagnose due to identical clinical signs of respiratory system. Aim: In this research, to diagnose this, a structure of a fuzzy expert system has been offered. This is done in order to help general physicians and the patients make decision and also differentiate among chronic bronchitis, tuberculosis, asthma, embolism, lung cancer. Methods: This system has been created using fuzzy expert system and it has been created in 4 stages: Definition of knowledge system, design of knowledge system, implementation of system, system testing using prototype life cycle methodology. Results: The system has 97 percent sensitivity, 85 percent specificity, 93 percent accuracy to diagnose the disease. Conclusion: Framework of the knowledge of specialist physicians using fuzzy model and its rules can help diagnose the disease correctly. © 2019 Leila Akramian Arani, Frahnaz Sadoughi, Mustafa Langarizadeh.

Item Type: Article
Additional Information: cited By 0
Uncontrolled Keywords: Article; asthma; chronic bronchitis; diagnostic accuracy; differential diagnosis; expert system; fuzzy logic; groups by age; human; knowledge base; lung cancer; lung embolism; major clinical study; pneumonia; sensitivity and specificity; tuberculosis
Subjects: Health Professions
Divisions: Faculty of Para medicine > Department of Management & Health Information Technology
Depositing User: ART . editor
Date Deposited: 29 Dec 2019 12:10
Last Modified: 29 Dec 2019 12:10

Actions (login required)

View Item View Item