Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86898
Title: Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events
Authors: Liu, Nan
Sakamoto, Jeffrey Tadashi
Cao, Jiuwen
Koh, Zhi Xiong
Ho, Andrew Fu Wah
Lin, Zhiping
Ong, Marcus Eng Hock
Keywords: Extreme Learning Machine
Ensemble Learning
Issue Date: 2017
Source: Liu, N., Sakamoto, J. T., Cao, J., Koh, Z. X., Ho, A. F. W., Lin, Z., et al. (2017). Ensemble-Based Risk Scoring with Extreme Learning Machine for Prediction of Adverse Cardiac Events. Cognitive Computation, 9(4), 545-554.
Series/Report no.: Cognitive Computation
Abstract: Accurate prediction of adverse cardiac events for the emergency department (ED) chest pain patients is essential in risk stratification due to the current ambiguity in diagnosing acute coronary syndrome. While most current practices rely on human decision by measuring clinical vital signs, computerized solutions are gaining popularity. We have previously proposed an ensemble-based scoring system (ESS). In this paper, we aim to extend the ESS system using extreme learning machine (ELM), a fast learning algorithm for neural networks. We recruited patients from the ED of Singapore General Hospital, and extracted features such as heart rate variability, 12-lead ECG parameters, and vital signs. We also proposed a novel algorithm called ESS-ELM to predict adverse cardiac events. Different from the original ESS algorithm, ESS-ELM uses the under-sampling technique only in model training. Our proposed method was compared to the original ESS algorithm and several clinical scores in predicting patient outcome. With a cohort of 797 recruited patients, we demonstrated that ESS-ELM outperformed the original ESS algorithm and three established clinical scores, namely HEART, TIMI, and GRACE, in terms of receiver operating characteristic analysis. Furthermore, we have investigated the impact of hidden node number and ensemble size on the predictive performance. ELM has demonstrated the flexibility in its integration with the ESS algorithm. Experiments showed the value of ESS-ELM in prediction of adverse cardiac events. Future works may include the use of new ELM-based learning methods and further validation with a new cohort of patients.
URI: https://hdl.handle.net/10356/86898
http://hdl.handle.net/10220/44244
ISSN: 1866-9956
DOI: http://dx.doi.org/10.1007/s12559-017-9455-7
Rights: © 2017 Springer. This is the author created version of a work that has been peer reviewed and accepted for publication by Cognitive Computation, Springer. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1007/s12559-017-9455-7].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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