Bijective soft set based classification of medical data

Abstract

Classification is one of the main issues in Data Mining Research fields. The classification difficulties in medical area frequently classify medical dataset based on the result of medical diagnosis or description of medical treatment by the medical specialist. The Extensive amounts of information and data warehouse in medical databases need the development of specialized tools for storing, retrieving, investigation, and effectiveness usage of stored knowledge and data. Intelligent methods such as neural networks, fuzzy sets, decision trees, and expert systems are, slowly but steadily, applied in the medical fields. Recently, Bijective soft set theory has been proposed as a new intelligent technique for the discovery of data dependencies, data reduction, classification and rule generation from databases. In this paper, we present a novel approach based on Bijective soft sets for the generation of classification rules from the data set. Investigational results from applying the Bijective soft set analysis to the set of data samples are given and evaluated. In addition, the generated rules are also compared to the well-known decision tree classifier algorithm and Naïve bayes. The learning illustrates that the theory of Bijective soft set seems to be a valuable tool for inductive learning and provides a valuable support for building expert systems.