Machine learning model for detecting masked hypertension in young adults
This approach could enable targeted interventions, particularly in resource-limited settings, thereby mitigating the progression of MHT and its associated risks. These findings underscore the importance of integrating advanced computational techniques into clinical practice to address global health challenges.
Authors
- William J. Richardson (ORCID: https://orcid.org/0000-0001-9608-199X)
- Annemarie Wentzel (ORCID: https://orcid.org/0000-0002-3765-2040)
- Samuel J. Coeyman
- Catharina M. C. Mels (ORCID: https://orcid.org/0000-0003-0138-3341)
- Brigitte Miller
Institutions
- North-West University (ZA)
- Wake Forest University (US)
- University of Arkansas at Fayetteville (US)
Publication Details
- Journal
- Frontiers in Physiology
- Published
- 2025-11-17
- DOI
- https://doi.org/10.3389/fphys.2025.1684693
- Citations
- 1
- Primary Topic
- Blood Pressure and Hypertension Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.39
Funders
- Pfizer
- GlaxoSmithKline
- Government of the United Kingdom
- National Research Foundation
- South African Medical Research Council
- Medical Research Council