A Study on the Prediction Model of Traumatic Hemorrhagic Shock Based on Machine Learning Algorithm

With the continuous development of the medical field and the rapid advancement of information technology, the application of medical data has become a crucial component of medical practice. The introduction of machine learning methods can analyse large amounts of medical data in a more detailed and rapid manner. By comprehensively evaluating patient demographic information, diagnosis, treatment, and other data in the electronic medical record, we can understand the patient’s health status and potential dangers, and provide a powerful auxiliary support for the doctor’s diagnosis and treatment. Traumatic haemorrhagic shock is a life-threatening clinical condition, and early and accurate prediction and intervention is essential for patient survival and recovery. In this study, for the injury condition of traumatic haemorrhagic shock, based on the emergency database of the General Hospital of the Chinese People’s Liberation Army, the inclusion and exclusion criteria of the research experiments were designed under the guidance of professional clinicians, from which the data of relevant patients’ medical indicators were extracted and data preprocessing was carried out. In the model construction stage, the study used five algorithms to construct a traumatic haemorrhagic shock prediction model using simple machine learning models including Logistic Regression, CART, and ensemble learning including Random Forest, XGBoost, and neural network structured multilayer perceptual machine MLP respectively, and the key indicators screened by doctors were used as the inclusion features. In the model evaluation stage, the study used a ten-fold cross-validation method to assess the prediction effect of different machine learning models by indicators such as ROC curve and AUC. The results showed that the XGBoost algorithm had the largest average AUC and the overall prediction effect was better than the other models, and the model had good prediction effect with potential clinical application. Finally, this study used the SHAP method to explain the value of different features to the prediction model from global and individual perspectives, making the model more credible.