Establishment of the prediction model for inhospital mortality of post-cardiac arrest patients based on machine learning
Lin Qingting
Zhang Nan
Jiang Hui
Zhu Huadong
Abstract：Objective To explore the relevant factors affecting the prognosis of patients with cardiac arrest and to establish an accurate and fast prognostic prediction model by machine learning.Methods Data from 1772 cardiac arrest patients over 18 years old from the medical information mart for intensive care(MIMIC)database were retrospectively analyzed and used to develop three machine learning models,including support vector machine(SVM),logistic regression(LR),and extreme gradient boosting(XGBoost)models,for predicting inhospital mortality.The areas under the receiver operating characteristic(AUC)curve,accuracy,precision value,recall value and F1 score were calculated to evaluate these models.Results In our study,the XGBoost algorithm outperformed the other algorithms.The accuracy,recall value,precision value and F1 score of the XGBoost algorithm were 0.762,0.812,0.765,and 0.788,respectively.In addition,the AUC of the XGBoost model was larger than that of the LR and SVM models(0.847 vs.0.834 and 0.820,respectively).The top 10 most important features of the XGBoost algorithm were minimum values of lactate,Glasgow coma scale(GCS),blood urea nitrogen,blood glucose,white blood cell,oxygen saturation and heart rate within 24 h after admission,and maximum values of temperature,creatine kinase-MB(CK-MB)and weight within 24 h after admission.Conclusions Compared with LR and SVM algorithms,the prediction model of cardiac arrest patients established by XGBoost algorithm in this study has more accurate prediction effect.
Keywords：Cardiac arrestInhospital mortalityMachine learningPrognosisLegistic regressionExtreme gradieat boostingSupport vector machine
Publication Date：2024-01-10
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 63-68 )