Fuzzy Expert System for Risk Assessment After Nipple-Sparing Mastectomy in Breast Cancer Patients

Nowadays, breast cancer is the most common malignancy in women around the world. It has been proven that a large number of new cases of breast cancer are identified each year. That increases the number of operations that impair the quality of women’s lives. Reconstruction after mastectomy, called nipple-sparing mastectomy (NSM), has become standard of women’s care. The risk assessment of complications after NSM requires a consideration of complex set of clinical and procedural factors. These factors can be expressed in qualitative and quantitative ways with some possible uncertainty. Fuzzy logic plays an important role in decision-making applications with imprecise and uncertain knowledge. The fuzzy inference system (FIS) modeled uncertain knowledge as a set of fuzzy rules and performs reasoning more precisely. This paper presents the FIS for the prognosis of risk assessment of complications in the NSM, providing a complex knowledge management and mitigating uncertainties in relation to different patients using fuzzy logic.