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.

该研究论文提出使用深度学习方法和特征增强技术评估患者是否有心血管疾病的风险，其结果优于其他最先进方法 4.4%，精度达到 90%，对于影响大量人口的疾病而言，这是一个显著的改进。