Knowledge Discovery Using Associative Classification for Heart Disease Prediction

作者: M. A. Jabbar , B. L. Deekshatulu , Priti Chandra

DOI: 10.1007/978-3-642-32063-7_4

关键词: Set (psychology)PopulationAssociation rule learningDiseaseCause of deathMachine learningData miningKnowledge extractionDecision support systemHeart diseaseArtificial intelligenceComputer science

摘要: Associate classification is a scientific study that being used by knowledge discovery and decision support system which integrates association rule methods to model for prediction. An important advantage of these systems that, using mining they are able examine several features at time. Associative classifiers especially fit applications where the may assist domain experts in their decisions. Cardiovascular deceases number one cause death globally. estimated 17.3 million people died from CVD 2008, representing 30% all global deaths. India risk more deaths due CHD. disease becoming an increasingly Andhra Pradesh. Hence proposed predicting heart patient. In this paper we propose new algorithm Pradesh population. Experiments show accuracy resulting set better when compared existing systems. This approach expected help physicians make accurate

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