Título
Extracting new patterns for cardiovascular disease prognosis
Autor
LUIS MENA CAMARE
JESUS ANTONIO GONZALEZ BERNAL
Nivel de Acceso
Acceso Abierto
Materias
Cardiovascular diseases - (CARDIOVASCULAR DISEASES) Machine learning - (MACHINE LEARNING) Blood pressure variability - (BLOOD PRESSURE VARIABILITY) Classification - (CLASSIFICATION) Medical decision support - (MEDICAL DECISION SUPPORT) Prognosis - (PROGNOSIS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
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.
Editor
Blackwell Publishing Ltd
Fecha de publicación
2009
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
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
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
343