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Predicting the Presence of Serious Coronary Artery Disease Based on 24 Hour Holter ECG Monitoring

机译:基于24小时HOLTER ECG监测预测严重冠状动脉疾病的存在

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The purpose of this study was to evaluate the usefulness of classification methods in recognizing a cardiovascular pathology. Based on clinical and electrocardiographic (ECG) Holter data we propose a method for predicting a coronary stenosis demanding revascularization in patients with a diagnosis of a stable coronary heart disease. A possible solution of this problem has been set in a context of rough set theory and methods. The rough set theory introduced by Zdzislaw Pawlak during the early 1980s provides a foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on a basis of a local discretization method, related to the problem of reducts computation. We present a new modification of a tree building method which emphasizes the discernibility of objects belonging to decision classes indicated by human experts. The presented method may be used to assess the need for the coronary revascularization. The paper includes results of experiments that have been performed on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Krakow, Poland.
机译:本研究的目的是评估分类方法在识别心血管病理学方面的有用性。基于临床和心电图(ECG)HOLTER数据,我们提出了一种预测患者患者血管无经血管无经血管内疾病的冠状动脉狭窄的方法。已经在粗糙集理论和方法的背景下设置了该问题的可能解决方案。 Zdzislaw Pawlak在20世纪80年代初推出的粗糙集理论为施工施工提供了课程。从粗糙集的透视图,本文呈现的分类器基于基于局部离散化方法计算的决策树,与计算计算问题有关。我们展示了一种树木建筑方法的新修改,它强调了属于人类专家指示的决策类的物体的可辨别。本方法可用于评估冠状动脉血运重建的需求。本文包括对从第二家内科,Collegium,Jagiellonian大学,克拉科夫,波兰的医学数据进行的医学数据进行的实验结果。

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