TITLE: Investigating the Synthetic Minority class Oversampling Technique (SMOTE) on an imbalanced cardiovascular disease (CVD) dataset 

ABSTRACT: 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.
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.
Investigating the Synthetic Minority class Oversampling Technique (SMOTE) on an imbalanced cardiovascular disease (CVD) dataset
http://arxiv.org/abs/2004.04101v1