Electrocardiogram Diagnosis Based on SMOTE+ENN and Random Forest
Many Electrocardiogram (ECG) classification algorithms have been successfully performed on standard dataset. Yet when faced with real world data, due to the issues of imbalanced data distribution, inconsistent data label formats, the performance of these algorithms are not ideal. In this paper, we propose an improved random forest algorithm, in which SMOTE+ENN is used to solve the data imbalance problem, while ECG medical knowledge including MIT BIH arrhythmia database expert annotations are adopted to align and create the real world data label. Experiments on ECG data from both standard dataset and real world dataset of a famous hospital showed the efficacy of the algorithm: the out of bag data (OOB) accuracy rate on the public data from MIT BIH arrhythmia database (MITDB) reached 99.22% and 96.62% on real world data.
ICIC