Asian Journal of Medicine and Health,
Aims: The risk factors for lung cancer are multiple. Smoking, Exposure to secondhand smoke, Exposure to radon gas, Exposure to asbestos and other carcinogens, Family history of lung cancer. However, what characterizes these factors is uncertainty and vagueness. Several analytical studies have been devoted to this domain, but the complexity of the environment makes it very difficult to model those using conventional mathematical or statistical tools. Also, the classical tools of analysis characterized by the vagueness. This insufficiency is compensated in our method by the fuzzyfication of the variables. In this study, age and sex are analyzed over a period from 2006 to 2014 according with the incidences recorded. Given the characteristic of the variables analyzed, a fuzzy logic system is proposed.
Methods: The input variables (age and sex) are fuzzyfied as well as the output variable which expresses the corresponding lung cancer incidence. By referring to the values recorded over the entire period, a rule base is established. The basis of the rules encompasses all possible combinations.
Results: The result is an algorithmic application which makes it possible to instantly read the expected incidence as a function of the values introduced randomly at the input of the system. The result at the output takes into consideration the collaboration of all the parameters at the input. Conclusion: As these factors are considered fuzzy. This makes it possible to have the most precise result possible. The presentation is symbolic and numerical and expressed instantaneously.