Predicting heart failure using data mining with Rough set theory and Fuzzy Petri Net

Abstract

The Rough Set Theory (RST) is a method that has proven its efficiency and simplicity in machine learning and successfully developing now a day’s vastly and rapidly. Fuzzy Petri nets (FPNs) are a potential modelling technique, which is used for knowledge representation and reasoning of rule-based expert systems. Though, RST has the efficiency in dimension reduction, it has to be proved with evidence by creating model using FPN with rule based reasoning. In this paper the induction of decision rules by RST executed with Fuzzy Petri Nets (FPN) is analyzed in the sense how it performed better than other data mining classifiers. The rule-based classifiers like jrip, part R, zero R are used for the comparison purposes. The knowledge captured from the rules through the best reduct has performed with the efficiency of RST and formulating rules by FPN. This paper experiments the Heart failure data to investigate the decision making from the rules generated by LERS system of RST with the approach of FPN. The heart failures during the follow up period of the patents are predicted with the pattern recognized form the data using the above process and the best evaluators are found during the experiments were pictured.