Compartir
Título
Heart disease risk prediction using deep learning techniques with feature augmentation
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Multimedia Tools and Applications
Cita Bibliográfica
García-Ordás, M.T., Bayón-Gutiérrez, M., Benavides, C. et al. Heart disease risk prediction using deep learning techniques with feature augmentation. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-14817-z
Editorial
Springer
Fecha
2023
ISSN
1380-7501
Resumen
[EN] Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.
Materia
Palabras clave
Peer review
SI
ID proyecto
- Junta de Castilla y León /LE078G18
URI
DOI
Aparece en las colecciones
- Artículos [4877]
Ficheros en el ítem
Tamaño:
1.484
xmlui.dri2xhtml.METS-1.0.size-megabytes
Formato:
Adobe PDF