When diagnosing a disease, the most difficulty thing doctors face is making an accurate decision to correctly determine the disease due to the similarity of the symptoms of different diseases. Therefore, in this research, using Pawlak's rough set model, upper approximation, lower approximation and by using nano-topology the factors affecting decision-making when afflicted with any disease were identified. Using the nano topology, we reduced the attributes in two real life situations by applying the knowledge as an information systems. Here, we have demonstrated by topological reduction of the decision criteria of a recent outbreak of "Hepatitis C" that fever and yellow skin and eyes are the most important indicators of the disease. It became clear from this, that the point of view of mathematical methods is completely consistent with the medical expert's point of view. The effectiveness of rough set theory as a novel mathematical technique for extrapolating inferences from data has been demonstrated. It appears that the rough set concept will soon find very intriguing new applications. These consist of rough control, rough data bases, rough information retrieval, rough neural network and others.