Heart disease risk prediction using deep learning techniques with feature augmentation

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 …

age augmentation cs.lg death deep learning deep learning techniques detection disease diseases feature general heart disease influence life patients population prediction rate risk risks sex state survival