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 in 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.