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Fuzzy Logic and Neural Networks for Disease Detection and Simulation in MATLAB

Authors

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

Abstract

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.

Keywords

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

Full Text  Volume 13, Number 23