Ischemic heart disease detection using selected machine learning methods
Ischemic heart disease detection using selected machine learning methods
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DOI：
10.1080/00207160.2012.742189
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发表时间：
2013-08-01
影响因子：
1.8
通讯作者：
Ciecholewski, Marcin
中科院分区：
文献类型：
--
作者：
Ciecholewski, Marcin
本文提出了一种基于支持向量机（svm）和Osuna-Platt算法的缺血性心脏病诊断方法。它还包括优化理论中必要的概念，这些概念使支持向量问题的表述成为可能，并应用了Osuna-Platt算法。其次，提出了一种主成分分析（PCA）算法。实验部分使用了单光子发射计算机断层扫描（SPECT）获得的心脏图像。本文将使用SVM、PCA和神经网络对心脏SPECT图像进行分类的结果与使用另一种机器学习方法CLIP3（决策树算法和规则归纳法的结合）获得的结果进行比较。针对SPECT图像数据库的测试表明，支持向量机通常更准确和具体，而PCA算法对本研究项目分析的所有数据集最敏感。
This article presents a method based on support vector machines (SVMs) and the Osuna-Platt algorithm used to diagnose the ischemic heart disease. It also includes the necessary concepts from the optimization theory which make it possible to formulate the problem for support vectors and the Osuna-Platt algorithm applied. Next, a principal component analysis (PCA) algorithm used is also presented. Heart images acquired using Single Photon Emission Computed Tomography (SPECT) have been used in the experimental part. Results of classifying cardiac SPECT images using SVM, PCA and neural networks are compared here with those obtained using another method of machine learning - CLIP3 - a combination of the decision tree algorithm and the rule induction algorithm. Tests against an SPECT image database have shown that SVMs are generally more accurate and specific, while the PCA algorithm is the most sensitive for all data sets analysed in this research project.