SAFS: A Deep Feature Selection Approach for Precision Medicine
This work addresses precision medicine for hypertension risk assessment in a specific demographic subgroup, but it appears incremental as it applies a known deep learning approach to a new dataset.
The paper tackled the problem of identifying significant risk factors for hypertension in African-American populations by developing a deep feature selection method using stacked auto-encoders, resulting in improved performance compared to other methods.
In this paper, we propose a new deep feature selection method based on deep architecture. Our method uses stacked auto-encoders for feature representation in higher-level abstraction. We developed and applied a novel feature learning approach to a specific precision medicine problem, which focuses on assessing and prioritizing risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach is to use deep learning to identify significant risk factors affecting left ventricular mass indexed to body surface area (LVMI) as an indicator of heart damage risk. The results show that our feature learning and representation approach leads to better results in comparison with others.