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Introduction <p>Determining the triage level of patients upon their arrival at the hospital emergency department is highly important for identifying high-risk patients and allocating resources to them. This issue can be of even greater importance in patients presenting with cardiac-related symptoms. This study was conducted to predict the triage level of patients presenting with cardiac-related symptoms using machine learning methods and to compare the performance of different approaches.</p> Methods <p>This prospective study was conducted in 2024 in three main steps. In the first step, a literature review was performed, and the factors influencing patient triage levels were extracted from previous studies. Then the identified factors from the literature review were presented to experts for their opinion, and the final influential factors were determined based on their feedback. In the second step, patient data were collected from the triage unit of a specialized cardiac hospital. In the third and final step, the collected data were preprocessed and then analyzed using Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB) machine learning methods.</p> Results <p>After reviewing the literature and surveying experts, the confirmed factors were finally identified, and data related to 1862 patients were collected. 52% of the participants in this study were male. RF achieved the highest performance with a test accuracy of 93.57%, Cohen’s Kappa of 0.82, and a weighted F1-score of 0.93, followed by GB (accuracy = 90.08%, Kappa = 0.73) and SVM (accuracy = 86.6%, Kappa = 0.66). The most influential factors on patients’ triage levels included: the type of high-risk condition that elevates a patient to Level 2, need for life-saving intervention, having high-risk conditions (what condition), chief complaint, level of consciousness, and diseases.</p> Conclusion <p>In this study, five machine learning models were utilized for the triage of patients presenting with cardiac-related symptoms. The results of the study indicated that these algorithms had a good ability to discriminate between patients with different triage levels. The Random Forest method performed slightly better than the other techniques. These techniques can be used to differentiate between low-risk and high-risk patients and to allocate resources to high-risk patients.</p>

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Predicting triage levels in patients presenting with cardiac-related symptoms: a comparison of supervised machine learning methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Amirhossein Yazdi,
  • Mohadeseh Noori,
  • Seyed Mohammad Ayyoubzadeh,
  • Sajad Naghdi,
  • Soheila Saeedi

摘要

Introduction

Determining the triage level of patients upon their arrival at the hospital emergency department is highly important for identifying high-risk patients and allocating resources to them. This issue can be of even greater importance in patients presenting with cardiac-related symptoms. This study was conducted to predict the triage level of patients presenting with cardiac-related symptoms using machine learning methods and to compare the performance of different approaches.

Methods

This prospective study was conducted in 2024 in three main steps. In the first step, a literature review was performed, and the factors influencing patient triage levels were extracted from previous studies. Then the identified factors from the literature review were presented to experts for their opinion, and the final influential factors were determined based on their feedback. In the second step, patient data were collected from the triage unit of a specialized cardiac hospital. In the third and final step, the collected data were preprocessed and then analyzed using Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Gradient Boosting (GB) machine learning methods.

Results

After reviewing the literature and surveying experts, the confirmed factors were finally identified, and data related to 1862 patients were collected. 52% of the participants in this study were male. RF achieved the highest performance with a test accuracy of 93.57%, Cohen’s Kappa of 0.82, and a weighted F1-score of 0.93, followed by GB (accuracy = 90.08%, Kappa = 0.73) and SVM (accuracy = 86.6%, Kappa = 0.66). The most influential factors on patients’ triage levels included: the type of high-risk condition that elevates a patient to Level 2, need for life-saving intervention, having high-risk conditions (what condition), chief complaint, level of consciousness, and diseases.

Conclusion

In this study, five machine learning models were utilized for the triage of patients presenting with cardiac-related symptoms. The results of the study indicated that these algorithms had a good ability to discriminate between patients with different triage levels. The Random Forest method performed slightly better than the other techniques. These techniques can be used to differentiate between low-risk and high-risk patients and to allocate resources to high-risk patients.