Medical Application Fuzzy Decision Information System

Decision-making in information systems often involves uncertainty and imprecision. Traditional methods, such as those based on classical Rough Set Theory and original Pawlak’s model may struggle to handle such complexities and various data set class types. Keeping in mind the importance of similarity or dissimilarity measures and applications in data mining, medical diagnosis, decision making, and pattern recognition. This study proposes a novel approach to estimative similarity or dissimilarity degree membership calculation with fuzzy decision-making systems, leveraging symmetry relationships. Our method aims to enhance decision-making accuracy and robustness by considering the inherent uncertainties present in real-world data. Experimental results for selected application of dataset represents a hypothetical medical diagnosis scenario demonstrate the superiority of our approach compared to existing techniques, making it a promising tool for various applications in information systems. By calculating estimative membership degrees based on these relationships and similarity or dissimilarity, our approach offers a more nuanced understanding of decision boundaries and uncertainties.