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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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| 2009-GonzalesBernalJesusA-Extracting new patterns for cardiovascular diseases prognosis.pdf | 348.89 kB | Adobe PDF | View/Open |