A Medical Diagnostic System Based On Similarity Measures Of Soft Sets

Dealing with uncertainty is a common problem in social sciences, naturalsciences and other areas. There are so many studies about dealing with uncertainty.One of them is soft set. After the soft set was presented, more and more scholarsattach great importance to the theory. Therefore it is widely applied in many fields.The system that we said in the paper is based on soft set and similarity measures.The soft set is put up by Russian scholar Molodtsov in1999, as a tool to deal withuncertainty. Without any restrictions in description of the object, it can greatlysimplify the decision-making process, especially the problem of the lack of substanceinformation. With setting parameters introduced into decision-making, it makes up forthe shortcomings of the various theories, so it gets much attention in the field ofuncertainty. Otherwise, we often want to compare two soft sets in tackling practicalproblems. On the basis of previous research, Pinaki Majumdar and S.K.Samantaextend the similarity measure to the soft set, and make a soft set similarity measure.This lays the foundation for the application of soft set similarity measure.The medical diagnostic system in the paper is designed to play a supporting rolein the diagnosis. According to symptoms of the disease, we get the patients’ data.Then we organize the data into a soft set. At the last, we calculate the soft set with thestandard model of disease. We can get the degree from the calculation, which use thesoft set similarity measure. If similarity between the two soft sets is very large, we candraw the conclusion that the patient has a great probability of getting the disease. Thepatient needs to get a detailed examination. If similarity is very small, we can drawthe conclusion that the patient has not got the disease. In addition, according to thepractical occasion, we improve the soft set similarity measure and give a weight toeach condition for highlighting the importance of every condition in the disease. Thissystem sheds a new light on the development of medical diagnostic system，whichaims at not only reducing the doctors’ workload but also improving diagnosticaccuracy.