A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study

Insights into Imaging

Table 4 Performance of five machine learning algorithms to predict HT in the training cohort

HT, hemorrhagic transformation; SGD, Stochastic Gradient Descent; SVM, Support Vector Machine; LR, Logistic Regression; RF, Random Forest; XGB, eXtreme Gradient Boosting; AUC, area under the receiver operator characteristic curve; ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value