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EFFICIENT MODEL FOR CHD USING ASSOCIATION RULE WITH FDS

Priyanka Palod, Jayesh Gangrade

Association rules are an energetic investigating area. Association rules characterize a promising method to search syndrome differentiation on modern India. Solitary of the most accepted approach to do data mining is determining association rules. The association innovation is an imperative research field in data mining. The mining association rule frequently has been adopts numerous models: support, confidence, interestingness. But this model can’t accurate measure the correlative degree between the precursor and the consequential of the rule by allocation. So we proposed a new mining model of association rules: support, coincidence, interestingness and investigate the significance of fluke by instance. We use this model in the data about coronary heart disease and obtained a lot of meaningful rules. Proposed a new model of supportcoincidence- interestingness base on the traditional model of support-confidence interestingness. Our propose model can quantitatively evaluate the correlation of rules and reduce many rules that have low support or have no correlation or have negative correlation. In our work we will conduct experiments on large real time to predict the diseases like Medication in Coronary Heart Disease and compare the performance of our algorithm with other related algorithms. Our propose model based on CMAR (Classification based on Multiple Association Rules) SVM, fuzzy discriminant Analysis.