Modeling Prediction of Hematoma Expansion in Hemorrhagic Stroke Based on Machine Learning

This article constructs a classification prediction model for the cerebral hematoma volume expansion based on clinical indicators, enabling various algorithms to achieve optimal generalization performance. First, we use the Random Forest algorithm based on information theory to rank the importance of clinical indicators by feature and obtain 20 efficient variables that can be used for algorithm judgment; Then we use BP Neural Network algorithm to achieve classification prediction of cerebral hematoma expansion. Also, data normalization, data cleaning, selection of loss function and L2 regularization are used to optimize the model, and the algorithm was evaluated through Roc Curve. The results indicate that the model can predict the occurrence of cerebral hemorrhage well, with an accuracy of 0.933, providing new ideas for clinical response to emergencies and treatment choices.