In diagnosis of COPD (Chronic Obstructive Pulmonary Diseases), spiromerty is an important "Pulmonary Function Testing" in the medical evaluation of patients. Spirometric measurements FVC & FEV1 are very important to control the treatment, but some difficulties such as incompleteness, inaccuracy and inconsistency are encountered during the test. "Fuzziness in Spirometry" is very important "real-world problem". Even if it is almost impossible to find ideal mathematical equations, ideal prediction formulas and ideal propositions defining the behaviors formulated ideally satisfying the real-life, it is possible to define inexact medical information and findings as fuzzy sets. Furthermore, because of collected data just lying on the border-line cannot be strictly or clearly defined either "normal" or "abnormal", the physicians may misinterpret some criteria or indications. For such kind of reasons, it is needed a formal model of distinguishing COPD group diseases (chronic bronchitis, emphysema and asthma) by using fuzzy theory and to put into practice a "fuzzy rule-base". Purpose of this study is to construct a fuzzy rule-base model for designing a "COPD Diagnosing Fuzzy Expert System by Classifying Spirometric FVC Plots".
