Intelligent Study of Haemorrhagic Stroke Based on BP Neural Network and Random Forest Model

This paper focuses on the hematoma expansion risk, the occurrence and evolution pattern of perihematomal edema, and the clinical prognosis of hemorrhagic stroke patients, and analyzes the real clinical data by using the random forest model, the K-means clustering model, and the BP neural network model. First, in the study for the risk of hematoma expansion, the probability model of hematoma expansion within 48 hours of onset after patient treatment was established by principal component analysis and random forest prediction model. Second, in the study on the occurrence and progression of perihematomal edema, K-means clustering algorithm and gray correlation analysis were used to further analyze the relationship between hematoma volume, edema volume, and treatment method. Finally, in the study of clinical prognosis, a BP neural network prediction model was constructed, and subsequent follow-up information was added to the model, and the prediction effect was significantly improved. Through this study, the risk of hemorrhagic stroke was found, and the integration of multi-party information for accurate prediction provided an important reference for optimizing clinical decision-making.