A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study

Frontiers in Neuroscience

Neuroscience

Chan Xu¹  Xiu’e Wei²  Liangqun Rong²  Kai Wang²  Haiyan Liu²  Chao Sun³  Wencai Liu⁴  Vicky Yau⁵  Chengliang Yin⁶  Chenyu Sun⁷  Wenle Li⁸  Qianqian Shi⁹ 

[1] Department of Dermatology, Xianyang Central Hospital, Xianyang, China;Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China;Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China;Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, China;Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China;Division of Oral and Maxillofacial Surgery, Columbia University Irving Medical Center, New York, NY, United States;Faculty of Medicine, Macau University of Science and Technology, Macau, China;Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China;Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China;State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China;State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China;

关键词: stroke;    recurrence;    machine learning;    SHAP;    web calculator;   

DOI  :  10.3389/fnins.2023.1130831

 received in 2022-12-23, accepted in 2023-02-27,  发布年份 2023

来源: Frontiers

【 摘 要 】

Background and purposeRecurrent stroke accounts for 25–30% of all preventable strokes, and this study was conducted to establish a machine learning-based clinical predictive rice idol for predicting stroke recurrence within 1 year in patients with acute ischemic stroke (AIS).MethodsA total of 645 AIS patients at The Second Affiliated Hospital of Xuzhou Medical University were screened, included and followed up for 1 year for comprehensive clinical data. Univariate and multivariate logistic regression (LR) were used to screen the risk factors of stroke recurrence. The data set was randomly divided into training set and test set according to the ratio of 7:3, and the following six prediction models were established by machine algorithm: random forest (RF), Naive Bayes model (NBC), decision tree (DT), extreme gradient boosting (XGB), gradient boosting machine (GBM) and LR. The model with the strongest prediction performance was selected by 10-fold cross-validation and receiver operating characteristic (ROC) curves, and the models were investigated for interpretability by SHAP. Finally, the models were constructed to be visualized using a web calculator.ResultsLogistic regression analysis showed that right hemisphere, homocysteine (HCY), C-reactive protein (CRP), and stroke severity (SS) were independent risk factors for the development of stroke recurrence in AIS patients. In 10-fold cross-validation, area under curve (AUC) ranked from 0.777 to 0.959. In ROC curve analysis, AUC ranged from 0.887 to 0.946. RF model has the best ability to predict stroke recurrence, and HCY has the largest contribution to the model. A web-based calculator https://mlmedicine-re-stroke2-re-stroke2-baylee.streamlitapp.com/ has been developed accordingly.ConclusionThis study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.

【 授权许可】

Unknown   
Copyright © 2023 Wang, Shi, Sun, Liu, Yau, Xu, Liu, Sun, Yin, Wei, Li and Rong.