A METHOD FOR MINING INFREQUENT CAUSAL ASSOCIATIONS WITH SWARM INTELLIGENCE OPTIMIZATION FOR FINDING ADVERSE DRUG REACTION

Due to the increasing growth of the population on now a day's analysis to medical drugs also plays most considerable process, finding the relationship among one drug to another drug are used to prevent unexpected outcomes of patients in efficient manner. Finding those relationships in efficient manner data mining plays most imperative role to mine relationship and their reactions in well organized manner. Though, mining these relationships is not easy task due to the complicatedness of confine causality amongst actions and the irregular natural world of the actions of concentration in this purpose. In order to overcome this problem in this paper proposed an efficient algorithm to mine their causal relationship in efficient manner. Specifically, we developed a new interestingness assess, restricted causal-leverage, based on hybrid fuzzy recognition- primed decision (HRPD) model. In this paper presents a novel work that initially creates a fuzzy membership function for that causal relationship among drug and their reactions of those selected patient records. In order to optimize fuzzy parameter by using swarm intelligence based Exponential Particle Swarm Optimization (EPSO) optimization framework .Each and every consider number of particles select a fuzzy membership function for identification of best causal relationship and their reactions of drug in efficient manner by using cue represented in fuzzy model . It improves the results of Mining Infrequent Causal Associations among drugs in well efficient manner because of using EPSO Algorithm for further analysis and examination present through drug safety professionals, progress the accuracy of

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