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This paper presents a novel risk stratification method usingextreme learning machine (ELM). ELM was integrated into a scoringsystem to identify the risk of cardiac arrest in emergency department(ED) patients. The experiments were conducted on a cohort of 1025critically ill patients presented to the ED of a tertiary hospital. ELM andvoting based ELM (V-ELM) were evaluated. To enhance the predictionperformance, we proposed a selective V-ELM (SV-ELM) algorithm. Theresults showed that ELM based scoring methods outperformed supportvector machine (SVM) based scoring method in the receiver operationcharacteristic analysis

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