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

Kai Wang; Qianqian Shi; Chao Sun; Wencai Liu; Vicky Yau; Chan Xu; Haiyan Liu; Chenyu Sun; Chengliang Yin; Xiu'e Wei; Wenle Li; Liangqun Rong

doi:10.3389/fnins.2023.1130831

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

Front Neurosci. 2023 Mar 27:17:1130831. doi: 10.3389/fnins.2023.1130831. eCollection 2023.

Authors

Kai Wang^{1

2}, Qianqian Shi³, Chao Sun⁴, Wencai Liu⁵, Vicky Yau⁶, Chan Xu⁷, Haiyan Liu^{1

2}, Chenyu Sun², Chengliang Yin⁸, Xiu'e Wei^{1

2}, Wenle Li^{2

3}, Liangqun Rong^{1

2}

Affiliations

¹ Department of Neurology, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
² Key Laboratory of Neurological Diseases, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China.
³ State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
⁴ Department of Neurosurgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
⁵ Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
⁶ Division of Oral and Maxillofacial Surgery, Columbia University Irving Medical Center, New York, NY, United States.
⁷ Department of Dermatology, Xianyang Central Hospital, Xianyang, China.
⁸ Faculty of Medicine, Macau University of Science and Technology, Macau, China.

Abstract

Background and purpose: Recurrent 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).

Methods: A 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.

Results: Logistic 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.

Conclusion: This study identified four independent risk factors affecting recurrence within 1 year in stroke patients, and the constructed RF-based prediction model had good performance.

Keywords: SHAP; machine learning; recurrence; stroke; web calculator.

Grants and funding

This study was supported by the Scientific Research Project of Jiangsu Health Committee (No. H2019054), the Xuzhou Science and Technology Planning Project (No. KC21220), the Science and Technology Development Fund of Affiliated Hospital of Xuzhou Medical University (No. XYFY202250), and the Shaanxi Provincial Health and Health Research Fund Project (No. 2022E006).