Application of Genetic Algorithm-Optimized BP Neural Network in Prognosis Prediction of Hemorrhagic Stroke


  • Jichang Xing
  • Hui Zhang



Genetic Algorithm, Backpropagation Neural Network, Hemorrhagic Stroke Prognosis, Modified Rankin Scale Score, Correlation Analysis


Clinical intelligent diagnosis and treatment of hemorrhagic stroke, as a combination of artificial intelligence and intelligent medical treatment, is more conducive to perfectly monitoring the pathological cycle changes of patients through data flow. Based on the BP neural network model, this paper constructs a neural network model based on GABP to predict the 90-day mRS Score of patients, and successfully accurately predicts the prognosis of patients with hemorrhagic stroke. Based on the relationship between the patient's prognosis and personal history, disease history, treatment methods and imaging features, the recommendations for clinical decision-making were made. In order to test the distribution of the data, a Shapiro-Wilk distribution test model was constructed to test whether there were significant differences in the distribution of multiple populations, and finally a conclusion was reached that 90-day mRS Was not related to gender. The GABP neural network model constructed in this paper overcomes the limitation of BP neural network in complex problems, and uses genetic algorithm to optimize the weight and threshold, which improves the performance and optimization efficiency of the model. Overall, the GABP-based neural network model represents an approach that integrates neural networks and genetic algorithms and has the potential to solve complex problems. Future research and development will help to further improve the performance, interpretability and applicability of the model, thus promoting its wide application in different fields.


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How to Cite

Xing, J., & Zhang, H. (2024). Application of Genetic Algorithm-Optimized BP Neural Network in Prognosis Prediction of Hemorrhagic Stroke. International Journal of Biology and Life Sciences, 6(2), 1-6.