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An Efficient Entropy Weighted Deviation Approach to Simplify Weighted Association Rules in Bio Medical Applications

机译:简化生物医学应用中的加权关联规则的有效熵加权偏差方法

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In today?s era prediction of diseases is made in the way of data mining that is providing big deal of finding hidden patterns in large data bases. This prediction is made on the basis of association rules which are large in number and consists of many looping of rules. This increases the complexity of the whole system to arrive a simple result. This association rules compares the parameters of tested data of a patient such as risk factors and arrives results of presence of disease by following some predictive rules which consists of branches in decision tree structure. So, researchers generate system that works on entropy weighted deviation approach having effective resource allocation and utilization of data in minimum cost. In future, the system provides an adept methods disease prediction in various bio-medical applications.
机译:在当今时代,疾病的预测是通过数据挖掘的方式进行的,该方法可以在大型数据库中找到大量隐藏的模式。该预测是基于关联规则进行的,该关联规则数量众多并且包含许多规则循环。这增加了整个系统的复杂度,从而得出简单的结果。该关联规则比较患者测试数据的参数(例如危险因素),并通过遵循一些由决策树结构中的分支组成的预测规则得出疾病存在的结果。因此,研究人员生成了一种基于熵加权偏差方法的系统,该方法具有有效的资源分配和最小成本的数据利用。将来,该系统将提供各种生物医学应用中疾病预测的熟练方法。

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