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Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

Abstract

Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient’s medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created.

Author(s)

seifedine kadry

Journal/Conference Information

Neural Computing and Applications ,DOI: 10.1007/s00521-020-04862-2, ISSN: 09410643, Volume: 98, Issue: 4, Pages Range: 1-14,