Objective To establish a 1-year functional outcome prediction model for new-onset acute ischemic
stroke (AIS) patients based on machine learning algorithms, to provide reference for related
research and clinical work.
Methods This study was based on the data of new-onset AIS patients from China national stroke
registry (CNSR) database. Based on machine learning [CatBoost model, XGBoost model, GBDT model, randomized forest model] and traditional logistic regression model, the 1-year poor prognosis (mRS≥3) prediction models for new-onset AIS patients were constructed. According
to the ratio of 7:3, the patients were randomly divided into training set and test set. The training
set was used for model training and parameter optimization, and the test set was used to evaluate
the prediction value of the models. The evaluation indicators were mainly the AUC in the
discrimination index and the Brier score in the calibration index.
Results A total of 8230 eligible patients were included, with a mean age of 64.4±12.8 years old
and 3113 females (38.7%), and 2360 patients with 1-year poor prognosis. Multivariate analysis
showed that aging, female, mRS≥3 before stroke onset, NIHSS score at admission and discharge,
limb dysfunction, history of peripheral vascular disease, blood glucose at admission, blood lipidregulating
drugs (with medications at discharge) , antiplatelet drugs (1-year medication compliance)
were predictors for 1-year poor prognosis. The AUC of Catboost, XGBoost, GBDT, random
forest and logistic regression models for predicting 1-year functional prognosis of new-onset AIS
patients were 0.857 (0.850-0.864), 0.856 (0.850-0.863), 0.856 (0.848-0.864), 0.853 (0.846-0.859)
and 0.846 (0.837-0.855), respectively. The prediction performance of machine learning-based
prediction models were all superior than that of logistic regression model (Catboost vs . logistic,
P =0.0130, XGBoost vs . logistic, P =0.0133, GBDT vs . logistic, P =0.0229, random forest vs . logistic,
P =0.0429), and the calibration of each model was good.
Conclusions The 1-year functional prognosis prediction models of new-onset AIS patients based
on machine learning algorithm had high predictive value, and the Catboost model has the best
prediction effect.
