Elvir Ćajić¹, Irma Ibriśimović², Alma Śehanović¹, Damir Bajrić¹ and Julija Śćekić³, ¹Bosnia and Herzegovina, ²University of Tuzla, Bosnia and Herzegovina, ³University of Belgrade, Serbia

This paper investigates the integration of fuzzy logic and neural networks for disease detection using the Matlab environment. Disease detection is key in medical diagnostics, and the combination of fuzzy logic and neural networks offers an advanced methodology for the analysis and interpretation of medical data. Fuzzy logic is used for modeling and resolving uncertainty in diagnostic processes, while neural networks are applied for in-depth processing and analysis of images relevant to disease diagnosis. This paper demonstrates the development and implementation of a simulation system in Matlab, using real medical data and images of organs for the purpose of detecting specific diseases, with a special focus on the application in the diagnosis of kidney diseases. Combining fuzzy logic and neural networks, simulation offers precision and robustness in the diagnosis process, opening the door to advanced medical information systems.

Fuzzy logic, Neural networks, Disease detection, Matlab simulation, Medical images, Diagnostics, Uncertainty, Modeling