Introduction: We aimed to develop tools to ease the burden on medical staff while improving patient care. The central question is whether Support Vector Machines (SVMs), trained solely on patients¿ self-reported data (symptoms, demographics, comorbidities), can achieve triage accuracy comparable to or surpassing traditional score-based systems in emergency departments under pandemic conditions. Methods: We propose an SVM-based approach to replicate COVID-19 triage using patient-provided information rather than laboratory tests. The system automates triage scoring, classifying patients into red, yellow, or green categories. A computer program records patient data and calculates scores, while physicians have the ability to override results based on their clinical judgment. Results: The system integrates clinical expertise, enhancing rather than replacing physicians¿ roles. It achieved over 98% accuracy in red-triage classification using less than 2.8% of training data. Conclusions: This SVM-based triage model shows promise for improving emergency department processes, accuracy, and patient outcomes in high-demand situations.
