摘要
Abstract
Objective To explore the risk factors related to the recurrence of minor ischemic stroke(MIS)within two years by using an interpretable machine learning model.
Methods General data,laboratory results,imaging,and other data of patients with MIS in the Department of Neurology,Shanxi Cardiovascular Hospital from July to December 2020 were retrospectively collected.The risk factors for recurrence were screened by univariate analysis.Synthetic minority oversampling technique-nominal continuous treated the imbalance in the data.The data set was divided into a training set and a test set in a ratio of 8:2.Grid search 10-fold cross-validation to build light gradient boosting machine(LightGBM)and support vector machine(SVM)models.Compared with the logistic regression(LR)model,the discrimination and calibration degree of the model were evaluated based on the AUC and calibration curve,respectively.The model with the best performance was interpreted by the Shapley additive explanation(SHAP)model.
Results A total of 520 patients with MIS were included in this study,and 93(17.9％)relapsed within two years.The AUC of LightGBM,SVM,and LR predicted recurrence within 2 years in the test set were 0.935(95％CI 0.896-0.973),0.833(95％CI 0.770-0.896),and 0.764(95％CI 0.691-0.835),respectively.The accuracy was 0.890,0.773,0.693,and the Brier score was 0.105,0.167,and 0.200,respectively.The results showed that the LightGBM model had the best performance.The top 5 features of the SHAP-based LightGBM explanatory model were diastolic blood pressure,age,diabetes mellitus,LDL-C,and smoking.
Conclusions The prediction effect of the LightGBM model established in this study is good,and it can provide a reference for predicting recurrence in patients with MIS within two years.SHAP interpretability helps clinicians better understand the reasons behind prediction model results and make more personalized and rational clinical decisions for patients with MIS.关键词
轻型缺血性卒中/复发/轻量梯度提升机/Shapley加性解释Key words
Minor ischemic stroke/Recurrence/Light gradient boosting machine/Shapley additive explanation分类
临床医学
