Artificial Intelligence and Machine Learning for Women’s Health Issues - Chapter 3 - Early stage prediction of endometriosis cancer using fuzzy machine learning technique

The traditional treatment for endometrial cancer is surgery in which the uterus, fallopian tubes, and ovaries are removed. Other treatments that are helpful to treat early-stage endometrial cancer include radiation therapy, chemotherapy, hormone therapy, targeted therapy, and immunotherapy. The main concern of artificial intelligence (AI) is to design intelligent systems that have human capabilities, such as thinking, learning, reasoning, problem solving, and so on. This could be accomplished through the determination of knowledge representation, rule-based systems, search, learning, and so on to solve real-world problems. Of these, knowledge is the main player, as it is the skeleton of all the methods. Knowledge representation has two different entities: facts and the representation of facts. Facts about situations must be represented logically so that the program can understand the situation effectively. Muticriteria decision-making techniques have been used for decision-making purposes across disciplines. In this chapter, we use an intuitionistic trapezoidal fuzzy prioritized weighted average (ITrFPWA) operator to select suitable treatment for early-stage endometrial cancer. We discuss the algorithm based on the given operator in the intuitionistic fuzzy environment step by step and then demonstrate its effectiveness by considering a hypothetical case study based on the intuitionistic fuzzy information gathered from three medical experts about the diseases and their likely remedies. The outcome of the algorithm is the ranking of the treatments for the given information.