Research on the Construction of Multivariate-Induced ischemic stroke prediction model based on medical big data

The incidence, mortality and morbidity of ischemic stroke are high, and there is a trend of younger people in recent years. For patients and medical staff, building a reliable and accurate ischemic stroke medical early warning model is of great practical significance for disease screening and prevention. In the era of big data that year, traditional statistical and data analysis methods can no longer meet the needs of intelligent medical early warning. Based on the Hadoop platform and combined with the parallel database technology, this paper analyzes the medical information data that affects the cerebral blood flow velocity, and builds a prediction model for the incidence of ischemic stroke on this basis. The model applies the timely feedback of cause analysis and correlation analysis to the construction of the prediction model, which provides a technical reference for intelligent medical early warning. This model not only realizes the huge storage of medical information, but also can be used for clinical medical prediction and individual self-screening of patients for long-term observation and prevention of ischemic stroke.