A fuzzy rule-base model for classification of spirometric FVC graphs in chronical obstructive pulmonary diseases

In diagnosis of COPD (Chronic Obstructive Pulmonary Diseases), spirometry 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." The 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.".