Evaluation and analysis of clinical outcome prediction for trauma patients based on machine learning

We analyzed the optimal model from 5 aspects: (1) receiver operating characteristic curve, (2) 5 indicators, (3) the trade-off between precision and recall, (4) scenario application: emergency settings, and (5) economic benefits. XGBoost was recommended as the first choice based on the performance, but external validation was required to ensure clinical applicability. SHAP analysis enhanced model transparency, helping rescue staff to understand the degree of influence of variables and provide a reference for clinical decision-making. Furthermore, key variables (such as ISS and AIS scores) aligned with medical consensus, verifying the credibility of the model. In the future, the researches should validate the model in real-world scenarios and continuously optimize features and algorithms to enhance practicality.