• Dhifaf Azeez, K B Gan, M A Mohd Ali, and M S Ismail.
• Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq.
• Technol Health Care. 2015 Jan 1; 23 (4): 419-28.

BackgroundTriage of patients in the emergency department is a complex task based on several uncertainties and ambiguous information. Triage must be implemented within two to five minutes to avoid potential fatality and increased waiting time.ObjectiveAn intelligent triage system has been proposed for use in a triage environment to reduce human error.MethodsThis system was developed based on the objective primary triage scale (OPTS) that is currently used in the Universiti Kebangsaan Malaysia Medical Center. Both primary and secondary triage models are required to develop this system. The primary triage model has been reported previously; this work focused on secondary triage modelling using an ensemble random forest technique. The randomized resampling method was proposed to balance the data unbalance prior to model development.ResultsThe results showed that the 300% resampling gave a low out-of-bag error of 0.02 compared to 0.37 without pre-processing. This model has a sensitivity and specificity of 0.98 and 0.89, respectively, for the unseen data.ConclusionWith this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.

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