Framing Fuzzy Rules using Support Sets for Effective Heart Disease Diagnosis

Significance and relevance of certain features are obtained by various techniques. Feature subset selection involves summarizing mutual associations between class decisions and attribute values in a pre-classified database. In this paper genetic algorithm is used to find the relevant set of features by optimizing the fitness function and using the operators like crossover and mutation. Fuzzy logic is a form of knowledge representation suitable for notions that cannot be defined precisely, but which depend upon their contexts. In this work the fuzzy rules are framed with the help of support sets. The classification done using fuzzy inference system provides results that are better than other techniques.

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