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

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

Mathematical Problems in Engineering, 2014, vol. 2014, 1-6

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.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:248938

DOI: 10.1155/2014/248938

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