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

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.