Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning
The ML-based prediction models demonstrated superior performance metrics compared to conventional LR and the RF is the optimal prediction model, providing an effective tool for risk stratification in primary stroke prevention in community settings.
Authors
- Na Yu (ORCID: https://orcid.org/0000-0002-1154-3348)
- Yi Li (ORCID: https://orcid.org/0009-0001-9519-9045)
- Wendi Zhu (ORCID: https://orcid.org/0009-0000-7378-2013)
- Rui Wang
- Yunxia Duan
- Hongyu Sun (ORCID: https://orcid.org/0009-0002-4118-4777)
- Peng Shen (ORCID: https://orcid.org/0000-0002-3765-2630)
- Yumei Sun (ORCID: https://orcid.org/0000-0001-9208-5870)
- Yu Zhu
Institutions
- Peking University (CN)
- China Rehabilitation Research Center (CN)
- Ningbo Science and Technology Bureau (CN)
- Ningbo Center for Disease Control and Prevention (CN)
- Peking University Third Hospital (CN)
- Center for Life Sciences (CN)
Publication Details
- Journal
- International Journal of Nursing Sciences
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1016/j.ijnss.2025.10.011
- Citations
- 1
- Primary Topic
- Acute Ischemic Stroke Management
- Type
- article
- Field-Weighted Citation Impact
- 1.35