文章基本信息

标题：Cardiovascular Disease Analysis Using Supervised and Unsupervised Data Mining Techniques
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作者：Fabio Mendoza Palechor ; Alexis De la Hoz Manotas ; Paola Ariza Colpas 等
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2017
卷号：12
期号：2
页码：81-90
DOI：10.17706/jsw.12.2.81-90
语种：English
出版社：Academy Publisher
摘要：Cardiovascular diseases are the main cause of death around the world. Every year, more people die from these diseases than from any other cause. According to World Health Organization data, in 2012 more than 17,5 million people died from this cause, and that represents 31% of all deaths registered worldwide. Data mining techniques are widely used for the analysis of diseases, including cardiovascular conditions, and the techniques used in the proposed method in this research are decision trees, support vector machines, bayesian networks and k-nearest neighbors. Apart from the previous techniques, it was necessary to use a clustering method for data segmentation according to their diagnosis. As a result, the Simple K-Means clustering method and the support vector machines technique obtained the best levels in metrics such as precision (97%), coverage (97%), true positive rate (97%) and false positive rate (0.02%), and this can be taken as evidence that the proposed method can be used assertively as decision making support to diagnose a patient with cardiovascular disease.
关键词：Bayesian networks; cardiovascular disease; K-Nearest neighbor; data mining; decision trees; support vector machines.