Why the study?
Evaluating cardiovascular disease risk is difficult for clinicians due to the large number of influencing clinical variables, and late detection reduces survival chances.
Do deep learning methods combined with feature augmentation improve the precision of cardiovascular disease risk prediction compared to state-of-the-art methods?
Do deep learning methods combined with feature augmentation improve the precision of cardiovascular disease risk prediction compared to state-of-the-art methods?
Deep learning combined with feature augmentation improves the precision of cardiovascular disease risk prediction to 90%, outperforming existing state-of-the-art methods.
Answers are grounded in this paper and cite their sources.
Should not yet change CVD risk prediction practice; leaves open prospective validation against arrhythmia outcomes.
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.
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García-Ordás et al. (2023) studied this question.
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