Related Experiment Video

Updated: Apr 16, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Secondary triage classification using an ensemble random forest technique

Dhifaf Azeez¹, K B Gan², M A Mohd Ali²

¹Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|March 21, 2015

Abstract

Background:

Triage 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.

Objective:

An intelligent triage system has been proposed for use in a triage environment to reduce human error.

Methods:

This 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.

Results:

The 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.

Conclusion:

With this combination, the random forest reduces the variance, and the randomized resembling reduces the bias, leading to the reduced out-of-bag error.

Keywords:

Decision support system emergency department random forest randomized resampling

Related Concept Videos

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

Receiver Operating Characteristic Plot

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

Heart Failure IV: Classification and Diagnostic Evaluation

Heart Failure IV: Classification and Diagnostic Evaluation

Heart failure can be classified in various ways, with the most common classifications based on physical activity limitations, disease progression, severity, and treatment strategies.The Functional Classification of Heart Failure divides patients into four categories based on physical activity limitation due to symptom burden.Class I: Patients in this class have cardiac disease but no physical activity limitations. Ordinary activities like walking, climbing stairs, or routine tasks do not cause...

Articles linked to this work by shared authors, journal, and citation graph.

Inclusive Search for Anomalous Single-Photon Production in MicroBooNE.

Physical review letters·2026

First Search for Dark Sector e^{+}e^{-} Explanations of the MiniBooNE Anomaly at MicroBooNE.

Physical review letters·2026

First Measurement of Charged-Current Muon-Neutrino-Induced K^{+} Production on Argon Using the MicroBooNE Detector.

Physical review letters·2026

Biogas production from different food waste using small-scale floating-drum-type anaerobic digester.

Bioprocess and biosystems engineering·2025

Search for an Anomalous Production of Charged-Current ν_{e} Interactions without Visible Pions across Multiple Kinematic Observables in MicroBooNE.

Physical review letters·2025

First Measurement of ν_{e} and ν[over ¯]_{e} Charged-Current Single Charged-Pion Production Differential Cross Sections on Argon Using the MicroBooNE Detector.

Physical review letters·2025

See all related articles