Analysis of Resampling Method for Arrhythmia Classification Using Random Forest Classifier with Selected Features

There are many developed techniques to be experienced for biomedical data mining. Due to the growing number of cardiac patients, many data are stored by different organizations. It helps to work with data mining for medical signals. Here In this work, an attempt for analysis and classification of arrhythmia cardiac disease is taken by authors. Data collected from the UCI repository to validate the purposed method. The features from the data are chosen using correlation-based feature selection (CFS) method due to the elimination of redundant data. For better processing and classification the selected data is to be resampled and is done in this work using random sampling. Random forest classifier is used for classification purpose. The performance is evaluated with resampled data and exhibited in result section. It is found that the average accuracy is 96% with random resampling technique.