Título:
Application of data mining techniques to relate cardiovascular risk and coronary calcium
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Autor:
Lujan, F.N
Cymberknop, Leandro Javier
Alfonso, Manuel Roberto
Legnani, Walter Edgardo
Armentano, Ricardo L
Cymberknop, Leandro Javier
Alfonso, Manuel Roberto
Legnani, Walter Edgardo
Armentano, Ricardo L
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Ponencia
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Año de publicación:
2016
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Resumen:
Introduction : Knowledge Discovery in Databases (KDD) constitutes a process that allows data sets to be modeled and analyzed in an automated and exploratory manner. In this sense, data mining can be considered the main core of this procedure. Objective: In this study, a classification of clinical subjects (cluster) based on the comparison of parameters associated to cardiovascular risk factors was performed by means of KDD-based algorithms. Materials and Methods: the K-means algorithm, Hierarchical Agglomerative Clustering and Kohonen s Self-organizing Maps were applied to the database in order to obtain relationships based on the dissimilarity of its constitutive fields. Results: Four different clusters were obtained, represented by a group of well-defined clustering rules. Conclusion : KDD can be used to extract relevant data from clinical databases, which are strongly correlated with well-known cardiovascular risk markers.
Descripción:
20mo. Congreso Argentino de Bioingeniería y 9as Jornadas de Ingeniería Clínica, San Nicolás de los Arroyos, Argentina. 28–30 October 2015
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IOP Publishing
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Journal of Physics: Conference Series, 705, 2016
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Citación:
Lujan, F N, Cymberknop, L J, Alfonso, M, Legnani, W, Armentano Feijoo, R. "Application of data mining techniques to relate cardiovascular risk and coronary calcium" Journal of Physics: Conference Series, 705, 2016. DOI 10.1088/1742-6596/705/1/012040
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Licencia Creative Commons Atribución (CC - By 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| LCALA16.pdf | — | 1.1 MB | Adobe PDF |