Development and interpretation of a machine learning predictive model for early cognitive impairment in hypertension associated with environmental factors
The XGBoost model developed based on personal factors and natural environmental factors can predict early cognitive impairment of hypertension with superior predictive performance. Larger population cohorts are needed in the future to validate these findings and potentially enhance the ability to identify the occurrence of early cognitive impairment in people with hypertension.
Authors
- Shimeng Lv (ORCID: https://orcid.org/0000-0003-0607-7028)
- Tongmao Zhao (ORCID: https://orcid.org/0000-0002-7304-0682)
- Jing Li (ORCID: https://orcid.org/0000-0002-8232-2219)
- Xia Zhong (ORCID: https://orcid.org/0000-0003-0010-9498)
- Guangheng Zhang (ORCID: https://orcid.org/0009-0008-3984-4610)
- D. H. Liu (ORCID: https://orcid.org/0009-0006-6383-6069)
- Huachen Jiao (ORCID: https://orcid.org/0000-0002-6830-4903)
Institutions
- Shandong University of Traditional Chinese Medicine (CN)
- Peking University (CN)
- Jining Traditional Chinese Medicine Hospital (CN)
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-08-29
- DOI
- https://doi.org/10.3389/fcvm.2025.1477185
- Citations
- 1
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.47