RT info:eu-repo/semantics/article
T1 Heart disease risk prediction using deep learning techniques with feature augmentation
A1 García Ordás, María Teresa
A1 Bayón-Gutiérrez, Martín
A1 Benavides Cuéllar, María del Carmen
A1 Aveleira Mata, José Antonio
A1 Benítez Andrades, José Alberto
A2 Ingenieria de Sistemas y Automatica
K1 Informática
K1 Deep learning
K1 Sparse autoencoder
K1 Convolutional neural network
K1 Heart disease
AB [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.
PB Springer
SN 1380-7501
LK http://hdl.handle.net/10612/15890
UL http://hdl.handle.net/10612/15890
NO 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
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 19-may-2024