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Discovering Interesting Associations in Gestation Course Data

机译:在妊娠课程数据中发现有趣的关联

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Finding risk factors in pregnancy related to neonatal hypoxia is a challenging task due to the informal nature and a wide scatter of the data. In this work, we propose a methodology for sequential estimation of interestingness of association rules with two sets of criteria. The rules suggest that a strong relationship exists between the specific sets of attributes and the diagnosis. We set up a profile of the pregnant woman with a high likelihood of hypoxia of the newborn that would be beneficial to medical professionals.
机译:由于非正式的性质和广泛的数据分散,寻找与新生儿缺氧有关的妊娠危险因素是一项艰巨的任务。在这项工作中,我们提出了一种使用两组标准对关联规则的趣味性进行顺序估计的方法。该规则表明,特定属性集与诊断之间存在密切关系。我们建立了孕妇缺氧的可能性,该缺氧对新生儿的缺氧可能性很大,这对医疗专业人员很有帮助。

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