Predictive value of Logistic regression and XGBoost models for dysphagia in patients with acute ischemic stroke
Objective To screen risk factors to construct a risk prediction model for dysphagia after acute ischaemic stroke,and to compare the advantages and disadvantages of XGBoost model and Logistic regres-sion model.Methods 573 patients with acute ischemic stroke in the hospital were selected from January to December 2022,and randomly divided into modelling group(n=401)and validation group(n=172).According to a 7:3 ratio risk factors for the occurrence of dysphagia were screened,and Logistic regression models and XGBoost models were established with variables that were statistically significant by univariate analysis,respectively.Internal validation was performed on the validation group dataset using the ten-fold cross-validation method,and the predictive efficacy of the 2 models was evaluated using calibration curves,subject work characteristic curves(ROC curves)and decision curves.Results The results of multifactorial Logistic regression analysis showed that age,NIHSS score,GCS score,BI index,brainstem lesions,dysarthria,aphasiaand pharyngeal reflexes(normal)were the influencing factors of dysphagia after acute ischemic stroke.The top 8 rankings of importance of features in the XGBoost model were age,BI index,NIHSS score,pharyngeal reflexes,TOAST typing,albumin,education level and nutritional score.Comparison of the results of the 2 models showed that the accuracy,precision,sensitivity and Fl score of the XGBoost model were 0.849,0.830,0.754 and 0.790,respectively,which outperformed the Logistic regression model.The AUC values of Logistic regression and the XGBoost model for the prediction of dysphagia were 0.894 and 0.925,respectively,and the difference was not statistically significant(P＞0.05).The cali-bration curve and clinical decision curve of the model showed that the accuracy and clinical utility value of the XGBoost model were better than that of Logistic regression model.Conclusion Both XGBoost model and Logistic regression model can effectively predict the risk of dysphagia after acute ischaemic stroke and the XGBoost model performs better,which can provide a reference for the clinical early prevention of dys-phagia in acute ischaemic stroke.
acute ischemic strokedysphagiaLogistic regressionXGBoost model
NETL
NSTL
万方数据
