Knowledge discovery approach to automated cardiac SPECT diagnosis
Knowledge discovery approach to automated cardiac SPECT diagnosis
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DOI：
10.1016/s0933-3657(01)00082-3
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发表时间：
2001-10-01
影响因子：
7.5
通讯作者：
Goodenday, LS
中科院分区：
文献类型：
--
作者：
Kurgan, LA;Cios, KJ;Goodenday, LS
介绍了一种利用数据挖掘和知识发现方法从心脏单质子发射计算机断层扫描(SPECT)图像中进行心肌灌注诊断的计算机化过程。我们使用六个步骤的知识发现过程。首先建立了一个数据库，该数据库由267张清洁的患者SPECT图像(约3000张2D图像)组成，并伴随着临床信息和医生的解释。然后，设计并实现了一种新的用户友好的诊断过程计算机化算法。SPECT图像被处理以提取一组特征，然后使用归纳机器学习和启发式方法生成显式规则来模仿心脏病专家的诊断。该系统能够为心脏SPECT研究提供一套计算机诊断，并可作为心脏病专家的诊断工具。由于诊断的高度正确率，所取得的结果令人鼓舞。(C)2001 Elsevier Science B.V.保留所有权利。
The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses. (C) 2001 Elsevier Science B.V. All rights reserved.