Prediction of cardiac arrhythmia type using clustering and regression approach (P-CA-CRA)

Cardiac Arrhythmia is a disease dealing with improper beating of heart. The improper condition may be fast beating or slow beating associated with heart. This paper proposes a detection or prediction scheme in the type of cardiac arrhythmia disease. It uses a clustering approach and regression methodology. The clustering approach used is DBSCAN and for regression, multiclass logistic regression is employed. By performing DBSCAN clustering algorithm, the whole dataset is disintegrated into disjoint clusters. Those clusters which are found to contain less instances, are then taken for consideration. These clusters are subjected to multiclass logistic regression. This is because, clustering approach is an unsupervised process. Once regression is performed, we have reached at a conclusion, about what type of cardiac arrhythmia it is. The proposed method achieves an overall accuracy of 80%, when compared with various other existing approaches.