ENSEMBLE METHODS FOR ECG-BASED HEARTBEAT CLASSIFICATION
Authors:
Rui Duan, Lakehead University, Canada
Sabah Mohammed, Lakehead University, Canada
Jinan Fiaidhi, Lakehead University, Canada
Rui Duan, Lakehead University, Canada
Sabah Mohammed, Lakehead University, Canada
Jinan Fiaidhi, Lakehead University, Canada
Ensemble method is a meta-algorithm to build strong classifiers based on a set of weak classifiers. This work explores some ensemble classifiers on UCI Arrhythmia Dataset to classify the heartbeat records. Each record corresponds to a person, and is the features extracted from the person’s raw ECG data over a period. Two popular ensemble classifiers XGBoost and RandomForest are used, and the classic LogisticRegression is tried as a comparison. These classifiers are applied on all the 279 features in the dataset, and predict the heartbeat categories for the records. XGBoost and RandomForest perform better than even well-tuned LogisticRegression. We build VotingClassifier based on the ensemble voting meta-algorithm and the above three built-in classifiers, and it outperforms even well-tuned XGBoost and RandomForest. The best prediction accuracy 76% is achieved by the VotingClassifier in this multiclass classification problem. This result is comparable to many other findings that uses similar or different classifiers.
ECG classification, XGBoost, Random Forest, Logistic Regression, Ensemble Method, Scikit-learn
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APA:
Duan, R., Mohammed, S., & Fiaidhi, J. (2019). Ensemble Methods for ECG-Based Heartbeat Classification. International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, 12(4), 29-46. doi: 10.33832/ijca.2019.12.4.03.
MLA:
Duan, Rui, et al. “Ensemble Methods for ECG-Based Heartbeat Classification.” International Journal of Control and Automation, ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 12, no. 4, 2019, pp. 29-46. IJCA, http://article.nadiapub.com/IJCA/vol12_no4/3.html.
IEEE:
[1] R. Duan, S. Mohammed, and J. Fiaidhi, "Ensemble Methods for ECG-Based Heartbeat Classification." International Journal of Control and Automation (IJCA), ISSN: 2005-4297 (Print); 2207-6387 (Online), NADIA, vol. 12, no. 4, pp. 29-46, Apr 2019.