Combining Data Mining Techniques to Enhance Cardiac Arrhythmia Detection

Abstract

Detection of Cardiac Arrhythmia (CA) is performed using the clinical analysis of the electrocardiogram (ECG) of a patient to prevent cardiovascular diseases. Machine Learning Algorithms have been presented as promising tools in aid of CA diagnoses, with emphasis on those related to automatic classification. However, these algorithms suffer from two traditional problems related to classification: (1) excessive number of numerical attributes generated from the decomposition of an ECG; and (2) the number of patients diagnosed with CAs is much lower than those classified as “normal” leading to very unbalanced datasets. In this paper, we combine in a coordinate way several data mining techniques, such as clustering, feature selection, oversampling strategies and automatic classification algorithms to create more efficient classification models to identify the disease. In our evaluations, using a traditional dataset provided by the UCI, we were able to improve significantly the effectiveness of Random Forest classification algorithm achieving an accuracy of over 88%, a value higher than the best already reported in the literature.