Research on Stroke Prediction Model Driven by Deep Learning for Predicting Blood Flow Velocity

In the era of big data, traditional statistical and data analysis methods can no longer meet the needs of intelligent medical warning. Building a reliable and precise medical early warning model is of great practical significance. The incidence rate, mortality and disability rate of ischemic stroke are very high, and there is a trend of youth in recent years. It is of great significance for patients and medical staff to construct an accurate traditional Chinese medicine warning model for ischemic stroke. Based on deep learning and Spark platform, combined with parallel database technology, this paper analyzes the medical information data related to cerebral blood flow velocity, and constructs a prediction model of ischemic stroke incidence rate. The model applies causal analysis, correlation analysis, and timely feedback to the construction of predictive models, providing an inevitable development trend for intelligent medical warning. This model not only achieves massive storage of medical information, but also has certain practical significance for long-term observation and prevention of ischemic stroke.