Určenie miery rizika rakoviny pankreasu pomocou fuzzy prístupov

Abstract

This thesis investigates the application of fuzzy logic approaches to improve the diagnostic accuracy of pancreatic cancer risk. Pancreatic cancer, notorious for its late diagnosis and poor prognosis, necessitates the development of more sophisticated diagnostic techniques. By integrating risk factors from patient histories and medical tests into a fuzzy model, this work seeks to address the uncertainties inherent in early cancer symptomatology. The thesis presents the design, implementation, and testing of a fuzzy logic-based model that estimates pancreatic cancer risk with increased sensitivity and specificity, compared to traditional methods. The findings highlight the potential of fuzzy systems in enhancing the predictive accuracy of medical diagnostics and underscore the importance of computational models in the early detection of complex diseases like pancreatic cancer.