Effects of discretization on determination of coronary artery disease using support vector machine

Ismail Babaoglu, O. Findik, Erkan Ülker
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引用次数: 1

Abstract

In this paper, the effect of discretization on determination of coronary artery disease using exercise stress test data by support vector machine classification method is investigated. The study dataset is obtained from cardiology department of Meram faculty of medicine including 480 patients having 23 features. Four classification models are composed. In the first model, the data is classified simply by normalizing it into [-1,1] range. In the second, third and fourth models, the data is classified by employing entropy-MDL, equal width and equal frequency discretization methods on it respectively. Support vector machine is used as the classifier for all classification models. The results show that classification performance of the model implemented by entropy-MDL discretization has the best value.
离散化对支持向量机冠状动脉疾病诊断的影响
本文采用支持向量机分类方法,研究离散化对运动应激试验数据诊断冠状动脉疾病的影响。研究数据集来自Meram医学院心内科,包括480例患者,共有23个特征。分为四种分类模型。在第一个模型中,通过将数据归一化为[-1,1]范围,简单地对数据进行分类。在第二、第三和第四模型中,分别采用熵- mdl、等宽和等频离散方法对数据进行分类。使用支持向量机作为所有分类模型的分类器。结果表明,采用熵- mdl离散化实现的模型分类性能最佳。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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