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Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients

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  • Nan Liu
  • Jiuwen Cao
  • Zhi Xiong Koh
  • Pin Pin Pek
  • Marcus Eng Hock Ong

Abstract

This paper presents a novel risk stratification method using extreme learning machine (ELM). ELM was integrated into a scoring system to identify the risk of cardiac arrest in emergency department (ED) patients. The experiments were conducted on a cohort of 1025 critically ill patients presented to the ED of a tertiary hospital. ELM and voting based ELM (V-ELM) were evaluated. To enhance the prediction performance, we proposed a selective V-ELM (SV-ELM) algorithm. The results showed that ELM based scoring methods outperformed support vector machine (SVM) based scoring method in the receiver operation characteristic analysis.

Suggested Citation

  • Nan Liu & Jiuwen Cao & Zhi Xiong Koh & Pin Pin Pek & Marcus Eng Hock Ong, 2014. "Risk Stratification with Extreme Learning Machine: A Retrospective Study on Emergency Department Patients," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-6, August.
    • Handle: RePEc:hin:jnlmpe:248938
    DOI: 10.1155/2014/248938
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