Machine learning model development
NB
REGRESSION
The present study applied four common machine learning (ML) algorithms to predict the 28-day in-hospital mortality among ICU elderly patients with IS, including Naïve Bayes (NB), eXtreme Gradient Boosting (xgboost), and logistic regression (LR). A validation set of 241 patients who never participated in the model training was used to evaluate all of those ML metrics. To improve the stability of the prediction model, all continuous features are rescaled to a distribution with a mean of 0 and standard deviation of 1, and the scale conversion is performed. After 10-fold cross-validation, the performances of the three prediction models were compared with each other using areas under the curve, specificity, sensitivity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. The best model was selected by the area under the receiver operating characteristic curve (AUC) values of the testing set.