Construction of risk prediction model for stroke in the patients with acute persistent vertigo based on random forest algorithm

Fu Jitong; Wang Tiantian; Zhang Jinping; Lian Rui

doi:10.3969/j.issn.1002-1949.2024.05.008

Abstract：Objective To construct a risk prediction model for stroke in the patients with acute persistent vertigo based on random forest algorithm.Methods 400 patients with acute persistent vertigo in Zhengzhou Seventh People's Hospital from January 2019 to December 2022 were selected retrospectively as research subjects,and were randomly divided into training set(240 cases)and validation set(160 cases)with a ratio of 6∶4.The patients who had not suffered from stroke in the final training set data were defined as 0 and those who had suffered from stroke were defined as 1,and the clinical data of the subjects were collected by using R 4.1.3 software.The random forest and other data packets in R 4.1.3 software were used to screen the influencing factors of stroke in the patients with acute persistent vertigo,and a prediction model based on random forest algorithm was constructed.The prediction model was visualized by drawing a nomograph,and the consistency index(C-index)and decision curve analysis were used to analyze the prediction results.Then the prediction efficiency of the model was verified by the validation set data.Results The random forest algorithm screened out the influencing factors of stroke in the patients with acute persistent vertigo,including four variables of central vertigo history,hypertension,atrial fibrillation history and hyperlipidemia.A prediction model based on the random forest algorithm was constructed,and the nomograph showed that the C-index of the prediction model in predicting stroke in the patients with acute persistent vertigo was 0.846(95%CI 0.760-0.910),and the absolute error of the calibration curve was 0.043.The C-index of validation set was 0.827(95%CI 0.738-0.895),and the absolute error was 0.048.The receiver operating characteristic(ROC)curve analysis showed that the area under the ROC curve(AUC)of prediction model for predicting the occurrence of stroke in the patients with acute persistent vertigo was 0.820,the sensitivity was 81.00%,and the specificity was 78.00%.When the threshold probabilities of the training set were 22%-100%by decision curve analysis,the clinical benefits after clinical intervention based on the predicted probability of the model within the range were higher than those of the patients with and without interventions.Conclusions The central vertigo history,hypertension,atrial fibrillation history and hyperlipidemia are the predictive factors for stroke in the patients with persistent vertigo.The prediction model based on random forest algorithm can be used for the prediction of stroke risks in the patients with acute persistent vertigo.