Asthma is a chronic lung disorder of which the number of suerers estimated to be
between 1.4-27.1% of the population in dierent areas of the world. Results of various studies show that
asthma is usually under-diagnosed, especially in developing countries, because of limited access to medical
specialist and laboratory data. The purpose of this paper is to design a fuzzy rule-based expert system to
alleviate this hazard by diagnosing asthma at initial stages. A knowledge representation of this system
is provided from a high level, based on patient perception, and organized into two dierent structures
called Type A and Type B. Type A is composed of six modules, including symptoms, allergic rhinitis,
genetic factors, symptom hyper-responsiveness, medical factors and environmental factors. Type B is
composed of 8 modules including symptoms, allergic rhinitis, genetic factors, response to short-term drug
use, bronchodilator tests, challenge tests, PEF tests and exhaled nitric oxide. The nal result of every
system is de-fuzzied in order to provide the assessment of the possibility of asthma for the patient.
Verication and validations criteria are considered throughout a life-cycle; the system was developed by
the participation of general physicians, experienced asthma physicians and asthmatic patients.