Radiography and Risk Factors of Lung Cancer: Modeling Using an Intelligent System

Abstract

Many factors involved in the detection of lung cancer. The confirmation depends on Age; Tobacco use; Historical malignancy; Growth rate; Edge characteristics; Size). The factors analyzed in this study are not precise beings and do not induce the same effects in the groups of the population. These variables are uncertain and therefore fuzzy variables. The principles of fuzzy inference prove adequate in this case. A fuzzy system is constructed with four input variables (Age, Tobacco use, Historical malignancy, density and morphological tumor) and one output represents the rate of exactitude in diagnosis. A base rules was established. It will be possible to read the expected degree of confirmation of malign tumor or benign tumor of lung cancer by introducing randomly the values in inputs variables. The analysis of factors at diagnosis, differ for the different types of lung cancer. Several detection radiography techniques to improve sensitivity for the representation of nodules modeling in association with other factors remain imprecise. The results of statistical analysis using Bayesian approach or logistic regression are limited to significant or insignificant with degree of incertitude. After the analysis system is done, the analysis system proposed takes into consideration uncertainties in these factors.