Optimization of Hemorrhagic Stroke Strategy Based on Random Forest and Apriori Algorithm

After a hemorrhagic stroke, patients may experience increased hematoma expansion and changes in edema volume. Timely and effective prognosis diagnosis and evaluation can improve the quality of life and survival rate of patients. This article is based on a data-driven approach, integrating imaging features and clinical patient information, and using random forest and Apriori algorithms for analysis, diagnosis, prediction, and evaluation. This provides strong support for clinical decision-making. The research results indicate that the model can effectively predict the probability of hematoma enlargement in patients and evaluate the influence of key factors such as edema volume.