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Managing Asthma in Children And Analyzing Best Possible Treatment With Data Mining Approach of Classification

机译:在儿童中管理哮喘,并用分类数据挖掘方法分析最佳疗效

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Data mining helps end users extract valuable information from large databases. In medical field, practioners have with them mountains of patient data. Any successful medical treatment is based after complete analysis of vast amount of patient data. But practitioners are often faced with the problem of extracting relevant information and finding certain trend or pattern that may further help them in the analysis of treatment of any disease. Data mining is such a tool, which sifts through voluminous data and presents the data of essential nature. In this paper we have focused on managing asthma in children. The approach used is C4.5 algorithm. Our predictive model will help in categorizing of asthma and also suggesting the best possible treatment. The choice of treatment is dependent on severity of the disease. The trick in building a successful predictive model is to have some data in the database that describes what has happened in the past. Classification method is designed to learn from the past successes and failures and then predict the outcome. Decision trees are a from of data mining technology that has been around for almost 20 years. They are increasingly used for prediction.
机译:数据挖掘帮助最终用户从大型数据库中提取有价值的信息。在医疗领域,实例有他们的患者数据山脉。任何成功的医疗后都是基于大量患者数据的完全分析。但是,从业者往往面临着提取相关信息的问题,并找到某些趋势或模式,这些趋势或模式可能在分析任何疾病的治疗方面进一步帮助他们。数据挖掘是这样的工具,它通过大量数据筛选并呈现基本性的数据。在本文中,我们专注于管理儿童哮喘。使用的方法是C4.5算法。我们的预测模型将有助于对哮喘进行分类,并表明最好的治疗方法。治疗的选择取决于疾病的严重程度。构建成功预测模型的技巧是在数据库中有一些数据,描述了过去发生的事情。分类方法旨在从过去的成功和失败中学习,然后预测结果。决策树是来自近20年的数据挖掘技术。它们越来越多地用于预测。

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