Research on an Intelligent Diagnostic and Prognostic Prediction Model for Hemorrhagic Stroke Based on Multi-View Data

由于出血性脑卒中起病急、进展快，预后较差，给社会患病患者及家属带来了沉重的负担，近年来引起临床广泛关注。出血性脑卒中主要有血肿扩张和血肿周围水肿发生和发展，早期发现并且提供有效的防治措施对患者治疗以及改善预后有很重要的意义。本题通过对于不同时期出血性脑卒中临床诊断的数据进行分析建模，使用聚类算法以及随机森林等方法比较出拟合程度最好的模型。Due to the rapid onset, progression, and poor prognosis of hemorrhagic stroke, it has brought a heavy burden to patients and their families in society, and has attracted widespread clinical attention in recent years. Hemorrhagic stroke mainly involves the occurrence and development of hematoma dilation and perihematoma edema. Early detection and effective prevention and treatment measures are of great significance for patient treatment and improving prognosis. This question analyzes and models clinical diagnosis data of hemorrhagic stroke at different stages, and compares the best fitting model using clustering algorithms and random forest methods.