Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method

The Random Forest model provides the most robust prediction of difficult laryngoscopy, outperforming both conventional and other ML methods. While ML models improve predictive accuracy, logistic regression remains a practical option in resource-limited settings. Integrating ML into clinical workflows could enhance decision-making, resource allocation, and patient safety in emergency airway management. Future research should prioritize external validation and real-world implementation.