Optimization for the CAD based on Fuzzy expert system
Rs3,000.00
10000 in stock
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People must be mindful of heart disease risk factors. Traditional approaches use thirteen risk factors or explanatory variables to classify heart disease. Heart disease diagnosis is a challenging task which can offer automated prediction about the heart disease of patient so that further treatment can be made easy. The present study proposes a new hybrid intelligent modeling scheme to obtain different sets of explanatory variables, and the proposed hybrid models effectively classify heart disease. The proposed hybrid models consist of CLPSO with fuzzy interference system. Proposes a new particle swarm optimization (PSO) based fuzzy expert system that involves four stages. In the first stage, the missing data are imputed using nearest neighbour hot deck imputation, while in the second stage, decision tree induction and set of rules is extracted from it. In the third stage, the crisp rules are transformed into fuzzy rule base using fuzzy membership functions. Finally, in the fourth stage, the fuzzy membership functions are tuned by PSO. The fuzzy model with the optimized parameters results in the final FES. Since the generated FES is based on the set of rules, they are able to provide interpretations for their decisions.
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