Fuzzy Logic Systems for Healthcare Applications

Abstract

Artificial Intelligence Technologies (AITs) have found application in several domains, including the area of medicine. Within this context, AITs have been leveraged for purposes such as illness diagnosis and treatment, patient monitoring, and risk evaluation. By using Artificial Intelligence Technologies (AITs), it becomes feasible to create systems that facilitate the development of intelligent models for predicting not only patients' response to therapy but also the risk of illness. Due to the intricate and uncertain nature of these domains, a multitude of scholars have developed AITs, including genetic algorithms, artificial immune systems, Artificial Neural Networks (ANN), and fuzzy logic. The integration of Fuzzy Logic Systems and ANN allows the construction of intelligent and flexible systems. ANN gain novel information by changing the connections among its distinct layers. Fuzzy logic inference frameworks provide a computational model that is grounded on fuzzy set rules, theory, and fuzzy reasoning. The amalgamation of many adaptive architectures gives rise to a "Neuro-Fuzzy" system. This research paper examines fuzzy network topologies, exploring their possible applications in the medical field. Researchers have recognized that this convergence has promise for the discovery of medical patterns.