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

Abstract

Soft Computing techniques play an important role for decision in applications with imprecise and uncertain knowledge. The application of soft computing disciplines is rapidly emerging for the diagnosis and prognosis in medical applications. Between various soft computing techniques, fuzzy expert system takes advantage of fuzzy set theory to provide computing with uncertain words. In a fuzzy expert system, knowledge is represented as a set of explicit linguistic rules.Diagnosis of breast cancer suffers from uncertainty and imprecision associated to imprecise input measures and incompleteness of knowledge of experts. However there are several technology-oriented studies reported for breast cancer diagnosis, few studies have been reported for the breast cancer prognosis. This research presents a fuzzy expert system for breast cancer prognosis to further support of the process of breast cancer diagnosis. This approach is capable enough to capture ambiguous and imprecise information prevalent in the characterization of the breast cancer. For this,the paper utilizes aMamdani fuzzy reasoning model, which has high interpretability for interacting with human expertsduringprognosis process and consequently early diagnosisof thediseased. The performance results on real patients' dataset reveal the accuracy of the system with an average 95% which shows the superiority of the system in the prognosis process compared to other related works.