Volume 12 Number 2 (Feb. 2017)
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JSW 2017 Vol.12(2): 81-90 ISSN: 1796-217X
doi: 10.17706/jsw.12.2.81-90

Cardiovascular Disease Analysis Using Supervised and Unsupervised Data Mining Techniques

Fabio Mendoza Palechor*, Alexis De la Hoz Manotas, Paola Ariza Colpas , Jorge Sepulveda Ojeda, Roberto Morales Ortega, Marlon Piñeres Melo
Universidad de la Costa, Barranquilla, Atlantico, Colombia.

Abstract—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.

Index Terms—Bayesian networks, cardiovascular disease, K-Nearest neighbor, data mining, decision trees, support vector machines.

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Cite: Fabio Mendoza Palechor*, Alexis De la Hoz Manotas, Paola Ariza Colpas , Jorge Sepulveda Ojeda, Roberto Morales Ortega, Marlon Piñeres Melo, "Cardiovascular Disease Analysis Using Supervised and Unsupervised Data Mining Techniques," Journal of Software vol. 12, no. 2, pp. 81-90, 2017.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jsw@iap.org
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