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Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke

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Abstract

Ischemic stroke is a serious disease posing significant threats to human health and life, with the highest absolute and relative risks of a poor prognosis following the first occurrence, and more than 90% of strokes are attributable to modifiable risk factors. Currently, machine learning (ML) is widely used for the prediction of ischemic stroke outcomes. By identifying risk factors, predicting the risk of poor prognosis and thus developing personalized treatment plans, it effectively reduces the probability of poor prognosis, leading to more effective secondary prevention. This review includes 41 studies since 2018 that used ML algorithms to build prognostic prediction models for ischemic stroke, transient ischemic attack (TIA), and acute ischemic stroke (AIS). We analyzed in detail the risk factors used in these studies, the sources and processing methods of the required data, the model building and validation, and their application in different prediction time windows. The results indicate that among the included studies, the top five risk factors in terms of frequency were cardiovascular diseases, age, sex, national institutes of health stroke scale (NIHSS) score, and diabetes. Furthermore, 64% of the studies used single-center data, 65% of studies using imbalanced data did not perform data balancing, 88% of the studies did not utilize external validation datasets for model validation, and 72% of the studies did not provide explanations for their models. Addressing these issues is crucial for enhancing the credibility and effectiveness of the research, consequently improving the development and implementation of secondary prevention measures.

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Funding

This research was supported by the following funding sources:  (1) Natural Science Foundation of Jilin Province (Grant no. 20230101263JC);  (2) Special Project of Health Talents of Jilin Province (Grant no. 2022SCZ19);  (3) Science and Technology Development Program of Jilin Province (Grant no. 20220204010YY, 20200201623JC, 20210401143YY);  (4) Science and Technology Development Program of Jilin Province (Grant no.20210401160YY, 20210101044JC).

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Authors and Affiliations

  1. School of Life Science and Technology, Changchun University of Science and Technology, Jilin Province, Changchun, 130022, People’s Republic of China

    Meng Chen, Dongbao Qian, Shuai Xu, Sheng Liu, Miao Li & Chunying Pang

  2. Union Hospital of Jilin University, Jilin Province, Neurosurgery, Changchun, 130033, People’s Republic of China

    Yixuan Wang, Junyan An, Ke Meng, Meiyan Sun & Miao Li

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  1. Meng Chen
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  9. Miao Li
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  10. Chunying Pang
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Contributions

All authors contributed to the design and conduct of the study. The idea of the investigation was conceived by Miao Li and Chunying Pang, who influenced its implementation. The first draft of the manuscript was written by Meng Chen, Dongbao Qian, Yixuan Wang, and Junyan An. The latest version of the manuscript and the graphics were prepared by Meng Chen and Dongbao Qian. The review and completion of the entire report were carried out by Meng Chen, Miao Li, and Chunying Pang. All authors have read and approved the final manuscript. Additionally, all authors participated in the coordination of the research, contributed to the writing, and approved the final document.

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Chen, M., Qian, D., Wang, Y. et al. Systematic Review of Machine Learning Applied to the Secondary Prevention of Ischemic Stroke. J Med Syst 48, 8 (2024). https://doi.org/10.1007/s10916-023-02020-4

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