A risk prediction model for epilepsy after cardiac arrest and resuscitation based on SMOTE algorithm
Zhang Jing
Lu Shenggui
Cai Baoyu
Zheng Yijing
Li Yixin
Abstract：Objective To explore the SMOTE algorithm based epilepsy risk prediction model after successful resuscitation of cardiac arrest.Methods A total of 200 patients with successful resuscitation of cardiac arrest admitted to the 910th Hospital of Joint Logistics Support Force of the Chinese People's Liberation Army from October 2016 to October 2023 were retrospectively collected as observation objects.According to whether epilepsy occurred after successful resuscitation,the patients were divided into epilepsy group(50 cases)and non-epilepsy group(150 cases).Data of the subjects were collected and analyzed.Univariate and multivariate Logistic regression analysis were used to screen the risk factors of epilepsy after successful resuscitation of cardiac arrest,and then the SMOTE algorithm was used to reconstruct the original data set of influencing factors to obtain the risk early warning model and verify its predictive efficiency.Results There were 50 cases of epilepsy after successful resuscitation in 200 cases of cardiac arrest,and the incidence was 25.0％.Multivariate Logistic regression analysis showed that age,history of epilepsy,intracranial infection,cardiovascular and cerebrovascular diseases,and hypoxic-ischemic brain injury were risk factors for epilepsy after successful resuscitation of cardiac arrest(all P＜0.05).The original early warning model P1=1.107X1+1.221X2+1.025X3+1.227X4+1.162X5-3.909 was obtained.Hosmer-Lemeshow test results showed that the model had a good fit(coefficient of determination R2=0.418,P=0.599).The early warning model based on the SMOTE algorithm was P2=1.212X1+1.351X2+1.208X3+1.357X4+1.271X5-4.241.The Hosmer-Lemeshow test results indicated that the model had a good fit(coefficient of determination R2=0.617,P=0.833).The results of receiver operating characteristic curve analysis showed that the area under the curve of the original warning model P1 was 0.791,and the area under the curve of the early warning model P2 was 0.825.Conclusion The SMOTE early warning model is better than the original warning model,and can accurately predict epilepsy after successful resuscitation of cardiac arrest.
Keywords：Successful resuscitation of cardiac arrestEpilepsySMOTE algorithmPrognostic model
Publication Date：2025-12-18
Online Publishing Date：2026-09-14(First online date of this platform, not the publication date of the document)
Pages：5( 1784-1788 )