A Rule based Fuzzy controlled Decision Support System for Management of Breast Cancer - Varshney, Monika; Srivastava, Azad Kumar; Aggarwal, Alok (International Journal of Computer Sciences and Engineering, 2025)
Breast cancer is one of the most common cancers all around the world and an early diagnosis of breast cancer plays a very vital role in the survival of the patient. Though there are plenty of experienced doctors, top range imaginary devices and advanced radiological techniques etc. but still computer assisted decision support system for the diagnosis of breast cancer can help a lot to medical staff for the said decease. This paper introduces a fuzzy logic (FL) based decision support system (DSS) for identifying the risk of breast cancer a person can have. The primary focus of the paper is on the algorithm used to identify the risk of breast cancer that a patient may have based on seven input parameters. The proposed system uses seven input parameters; namely age, genetic factor, menopause age, HER2, age of first pregnancy, alcohol intake & body mass index (BMI) which is based on diagnosis risk degree and one output which identify risk status of breast cancer recurrence or mortality in early diagnosed patients. Different medical practitioners dealing with the said decease were consulted before setting up the rule base. Through decision support system, the meaning of transferred data is translated into linguistic variables that can be understood by non-experts. Mamdani inference engine is used to deduce from the input parameters to stage the risk level of breast cancer