This study presents a precise mathematical framework to support the diagnosis of systemic lupus erythematosus using topological rough set theory. Topological rough sets offer a robust framework for handling incomplete information within an information system using topological tools. Central to this theory are the notions of upper and lower approximations related to a defined universal set. The concepts of upper and lower approximation, associated with a comprehensive set of specific symptoms, are essential to this theory. We focus on applying them to real-life cases suspected of having lupus based on some of their symptoms. We propose several measures, such as accuracy, dependency quality, approximate membership, and attribute significance, to assess medical uncertainty. We apply these criteria to a clinical model consisting of clinical cases. This approach has demonstrated the ability to improve diagnosis, particularly in ambiguous cases, and to identify key symptomatic indicators, thus facilitating more accurate clinical decision-making............ Key words:............ Rough set, lower (upper) approximations, boundary region, rough topology, positive( negative) regions, accuracy of approximation, dependency degree, attributes importance, reduct and core.