Heart disease risk prediction using deep learning techniques with
  feature augmentation
María Teresa García-Ordás Martín Bayón-Gutiérrez Carmen Benavides Jose Aveleira-Mata José Alberto Benítez-Andrades
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.