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An Analysis of the Survivability in SEER Breast Cancer Data Using Association Rule Mining

机译:基于关联规则挖掘的SEER乳腺癌数据生存性分析

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Medical professionals need a reliable methodology to predict the survivability of patients with breast cancer. In this work, a classical association rule mining algorithm-Apriori was adopted for analyzing the related association relationship between medical attributes of records and the survivability of patients. The SEER Dataset was used in this research. After the dataset was preprocessed, 29606 records was obtained. Each record contains 17 breast cancer related attributes. Then apriori algorithm was applied in these preprocessed records, 326 association rules about 'survived' and 22 association rules about 'not survived' were obtained finally. These discovered association rules indicate that the attributes of EOD-Lymph Node Involv and SEER historic stage A play important roles in the survivability of patients after analyzed and compared.
机译:医学专业人员需要一种可靠的方法来预测乳腺癌患者的生存能力。在这项工作中,采用经典的关联规则挖掘算法-Apriori分析记录的医疗属性与患者生存能力之间的关联关系。 SEER数据集用于本研究。数据集经过预处理后,获得29606条记录。每个记录包含17个与乳腺癌相关的属性。然后在这些预处理记录中应用先验算法,最终获得326条关于“幸存”的关联规则和22条关于“未幸存”的关联规则。这些发现的关联规则表明,经过分析比较,EOD淋巴结累及和SEER历史A期的属性在患者的生存能力中起着重要作用。

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