Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAFS: A Deep Feature Selection Approach for Precision Medicine   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.

8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg