Interpretable Machine Learning for Emergency Department Triage: Clinical Insights from 133,198 Patients Using the...

Background/Objectives: Emergency room severity classification (KTAS) is essential for patient safety but has limitations due to its reliance on subjective judgment. Existing machine learning models have not been trusted in clinical settings due to their opaque 'black box' nature in decision-making processes. To overcome this, this study aims to develop an explainable machine learning framework that provides a transparent basis for judgment with high accuracy. Method: We retrospectively analyzed 133,198 emergency room visits from 2022 to 2024. We trained Random Forest (RF) and XGBoost models using vital signs and pain scores and applied explainable AI (XAI) techniques to ensure model transparency. Results: Although XGBoost showed the highest predictive performance (94.7% accuracy within a ±1 error margin), we ultimately selected the RF model, which provides a good balance of predictive power (91.6%) and interpretability for clinical use. The results of the XAI analysis confirmed that pain score, age, and systolic blood pressure were the key variables in prediction, strongly aligning with clinical logic. Conclusions: This study demonstrates that explainable AI can provide transparent insights for KTAS prediction beyond the limitations of traditional black-box models. These models may support emergency department triage by improving consistency and assisting clinicians in identifying potentially high-risk patients. However, further external validation is required before routine clinical implementation.