Machine Learning–Based Prediction for Incident Hypertension Based on Regular Health Checkup Data: Derivation and Validation in 2 Independent Nationwide Cohorts in South Korea and Japan
Comparative evaluation of our machine learning models against classical statistical models across 2 distinct studies emphasized the former's enhanced stability, generalizability, and reproducibility in predicting hypertension onset.
Authors
- Jun Hyuk Lee (ORCID: https://orcid.org/0009-0006-0358-4760)
- Sang Youl Rhee (ORCID: https://orcid.org/0000-0003-0119-5818)
- Hayeon Lee (ORCID: https://orcid.org/0009-0000-2403-6241)
- Dong Keon Yon (ORCID: https://orcid.org/0000-0003-1628-9948)
- Ai Koyanagi (ORCID: https://orcid.org/0000-0002-9565-5004)
- Lee Smith (ORCID: https://orcid.org/0000-0002-5340-9833)
- Seung Ha Hwang (ORCID: https://orcid.org/0009-0004-0066-3047)
- Myeongcheol Lee (ORCID: https://orcid.org/0009-0006-7185-9471)
Institutions
- University of Southern California (US)
- Kyung Hee University (KR)
- Kyung Hee University Medical Center (KR)
- Parc Sanitari Sant Joan de Déu (ES)
- Anglia Ruskin University (GB)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2024-11-05
- DOI
- https://doi.org/10.2196/52794
- Citations
- 11
- Primary Topic
- Blood Pressure and Hypertension Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.42