A Combined-Mode Machine Learning Model for Predicting Stroke Recurrence During Hospitalization in Patients with Acute Minor Ischemic Stroke.

Journal: MedComm
Published Date: Jun 1, 2025

Abstract

Acute minor ischemic stroke patients often experience recurrence shortly after symptom onset, highlighting the importance of predicting stroke recurrence for guiding treatment decisions. This study evaluated the effectiveness of machine learning models in predicting in-hospital recurrence. The study cohort comprised 322,135 patients with acute minor ischemic stroke from 1439 centers, as established by Chinese Stroke Center Alliance. Patients were randomly allocated into training and test sets by different centers. Models including extreme gradient boosting (XGB), light gradient boosting (LGB), and adaptive boosting (ADA) were developed using fivefold cross-validation on the training set. Optimization was performed for all models based on the most important variable, history of ischemic stroke. Compared with the traditional generalized linear model (GLM), the XGB, LGB, ADA models yielded area under the curve (AUC) values ranging from 0.788 to 0.803 after optimization. All models showed significant improvements in AUC compared with GLM, with LGB exhibiting the most substantial enhancement after optimization. For the first time, this study developed models specifically designed to predict in-hospital stroke recurrence in acute minor ischemic stroke patients. This finding aids in identifying high-risk patients and prompts physicians to provide targeted treatment. However, further external validation is warranted to confirm the model's generalizability.

Authors

  • Wanxing Ye
    China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China (N.L., W.Y., J.J., Y.J., X.Z.).
  • Jin Gan
    Beijing Tiantan Hospital Capital Medical University Beijing China.
  • Meng Wang
    State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150001, China.
  • Ziyang Liu
    Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, USA.
  • Hongqiu Gu
    Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No. 119 South 4th Ring West Road, Fengtai District, Beijing 100070, China.
  • Xin Yang
    Department of Oral Maxillofacial-Head Neck Oncology, Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology & Shanghai Research Institute of Stomatology, Shanghai, China.
  • Chunjuan Wang
    Department of Neurology, Beijing Tiantan Hospital, Beijing, China.
  • Xia Meng
    School of Public Health, Fudan University, Shanghai 200032, People's Republic of China.
  • Yong Jiang
    Department of Pathology West China Hospital Sichuan University Chengdu China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Liping Liu
    Department of Computer Sciences, Tufts University, Medford, MA.
  • Yongjun Wang
    Department of Neurology, Beijing Tiantan Hospital, Beijing, China.
  • Zixiao Li
    Beijing Tiantan Hospital, Capital Medical University, Beijing, China. lizixiao2008@hotmail.com.

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