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Feature selection and classification using support vector machine and decision tree
Computational Intelligence ( IF 2.8 ) Pub Date : 2020-02-25 , DOI: 10.1111/coin.12280
B. Durgalakshmi 1 , V. Vijayakumar 1
Affiliation  

Breast cancer is one of the human threats which cause morbidity and mortality worldwide. The death rate can be reduced by advanced diagnosis. The objective of this article is to select the reduced number of features the help in diagnosing breast cancer in Wisconsin Diagnostic Breast Cancer (WDBC). This proposed model depicts women who all have no cancer cells or in benign stage later develop into malignant (metastases). Due to the dynamic nature of the big data framework, the proposed method ensures high confidence and low execution time. Moreover, healthcare information growth chases an exponential pattern, and current database systems cannot adequately manage the massive amount of data. So, it is requisite to adopt the “big data” solution for healthcare information.

中文翻译:

使用支持向量机和决策树进行特征选择和分类

乳腺癌是导致全球发病率和死亡率的人类威胁之一。先进的诊断可以降低死亡率。本文的目的是在威斯康星州诊断性乳腺癌 (WDBC) 中选择有助于诊断乳腺癌的特征数量减少。这个提议的模型描绘了没有癌细胞或处于良性阶段的女性,后来发展为恶性(转移)。由于大数据框架的动态特性,所提出的方法确保了高置信度和低执行时间。此外,医疗信息增长呈指数增长,当前的数据库系统无法充分管理海量数据。因此,采用“大数据”的医疗保健信息解决方案势在必行。
更新日期:2020-02-25
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