Clinical research and application of predicting the prognostic model of aneurysmal subarachnoid hemor-rhage based on machine learning
OBJECTIVE To construct a prediction model based on machine learning algorithm for predicting the prognosis of aneurysmal subarachnoid hemorrhage(aS AH).METHODS A total of 326 patients with aSAH treated in Tianjin Huanhu Hospi-tal from October 2020 to September 2021 were reviewed.According to the ratio of 7∶3,all the data were randomly divided into training set(to construct the prediction model)and test set(to evaluate the prediction model).SMOTE was used to deal with the imbalance data.Least absolute shrinkage and selection operator(Lasso)analysis was used to select the optimal variables.Logistic regression(LR),BP neural network,K near neighbor(KNN),random forest(RF),decision tree(DT),support vector machine(SVM),naive Bayes(NB)and XGBoost algorithm based on machine learning were used to construct the predictive model.RESULTS Eleven optimal features were obtained by Lasso regression.The accuracy of LR,BP neural network,KNN,RF,DT,SVM,NB and XGBoost model were 0.847,0.847,0.816,0.867,0.806,0.827,0.745 and 0.816,and AUC were 0.784,0.794,0.646,0.821,0.499,0.765,0.737 and 0.676,respectively.CONCLUSION Machine learning models are relatively more effective in predicting aS AH prognosis,and the RF model exhibits the best performance.
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