Please use this identifier to cite or link to this item: http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1180
Extracting new patterns for cardiovascular disease prognosis
LUIS MENA CAMARE
JESUS ANTONIO GONZALEZ BERNAL
Acceso Abierto
Atribución-NoComercial-SinDerivadas
Cardiovascular diseases
Machine learning
Blood pressure variability
Classification
Medical decision support
Prognosis
Cardiovascular diseases constitute one of the main causes of mortality in the world, and machine learning has become a powerful tool for analysing medical data in the last few years. In this paper we present an interdisciplinary work based on an ambulatory blood pressure study and the development of a new classification algorithm named REMED. We focused on the discovery of new patterns for abnormal blood pressure variability as a possible cardiovascular risk factor. We compared our results with other classification algorithms based on Bayesian methods, decision trees, and rule induction techniques. In the comparison, REMED showed similar accuracy to these algorithms but it has the advantage of being superior in its capacity to classify sick people correctly. Therefore, our method could represent an innovative approach that might be useful in medical decision support for cardiovascular disease prognosis.
Blackwell Publishing Ltd
2009
Artículo
Inglés
Estudiantes
Investigadores
Público en general
Mena-Camare L., et al., (2009). Extracting new patterns for cardiovascular disease prognosis, Expert Systems The Journal of Knowledge Engineering, Vol. 26 (5): 364-377
CIENCIA DE LOS ORDENADORES
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Artículos de Ciencias Computacionales

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