Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Investigating the Synthetic Minority class Oversampling Technique
  (SMOTE) on an imbalanced cardiovascular disease (CVD) dataset   In this work, we employ the Synthetic Minority Oversampling Technique (SMOTE)
to generate instances of the minority class of an imbalanced Coronary Artery
Disease dataset. We firstly analyze the public dataset Z -- Alizadeh Sani, a
dataset used for non-invasive prediction of CAD. We perform feature selection
to exclude attributes unrelated to Coronary Artery Disease risk. The generation
of new samples is performed using SMOTE, a technique commonly employed in
machine learning tasks. We design Artificial Neural Networks, Decision Trees,
and Support Vector Machines to classify both the original dataset and the
augmented. The results demonstrate that data augmentation may be beneficial in
specific cases, but it is not a panacea, and its application in a specific
dataset should be carefully examined.

26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas