A Fuzzy Rule-based Expert System for the Prognosis of the Risk of Development ofthe Breast Cancer

Soft computing techniques play an important role for decision-making applications with imprecise anduncertain knowledge. The application of fuzzy soft computing applications is rapidly emerging in themedical diagnosis and prognosis. A fuzzy expert system models knowledge as a set of explicitlinguistic rules and performs reasoning with words. Although there are several technology-orientedstudies reported for breast cancer diagnosis, few studies have been reported for the breast cancerprognosis. However, prognosis of breast cancer suffers from uncertainty and imprecision associated toimprecise input measures and incompleteness of knowledge as well as diagnosis. This researchpresents a fuzzy expert system for breast cancer prognosis. This approach is capable enough to captureambiguity and imprecision prevalent in the characterization of the breast cancer. For this, the paperutilizes a Mamdani fuzzy inference model, which is more intuitive and has high interpretability forinteracting with human experts during prognosis process. The main advantage of this work comparedto other related studies, mostly presented for assessing the risk of the cancer development stage, isusing unbiased input variables in the prognosis process; i.e., this model has the potential to predict therisk of developing breast cancer even in healthy females. Furthermore, the fuzzy expert system wasevaluated on real dataset and the results of system were compared to an obstetrician decisions. Theperformance results on real dataset reveals superiority of the fuzzy expert system in the prognosisprocess with an average accuracy of 95%, compared to other related works. This approach is optimisticfor prediction of breast cancer risk and early diagnosis of the cancer and can consequently improvesurvival rate