Risk Assessment of hematoma expansion in patients with hemorrhagic Stroke based on Stochastic Forest Prediction Model

Abstract

This paper discusses the prediction model of hematoma expansion probability in patients with hemorrhagic stroke. Hemorrhagic stroke is an acute and critical neurological disease. High mortality and disability make it a major challenge in the field of public health. Hematoma dilatation and peri-hematoma edema are important factors leading to secondary brain injury. therefore, accurate prediction of the risk of hematoma expansion in patients with hemorrhagic stroke has important guiding significance for clinical diagnosis and treatment. First of all, this study carries on the matching integration and abnormal processing of the patient data in order to improve the data quality. Then, a classification model based on support vector machine (SVM) and random forest is constructed to predict the probability of hematoma expansion. Through the comparative analysis of the classification accuracy, it is found that the prediction effect of the classification model based on random forest is better than that based on SVM. This result provides clinicians with a more accurate prediction tool, which is helpful to identify high-risk patients early, reduce the incidence of secondary brain injury, and improve the quality of life and prognosis of patients. This study provides a new idea for the clinical diagnosis, treatment and research direction of hemorrhagic stroke, and has important theoretical and practical value.