This paper is in the following e-collection/theme issue:

Artificial Intelligence (2162) Machine Learning (2095) Cardiology (228) Hypertension Prevention and Treatment (300) Cardiac Risk and Cardiac Risk Calculators (107) Advanced Data Analytics in eHealth (213) Clinical Information and Decision Making (1935) Decision Support for Health Professionals (1555)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52794, first published .
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

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

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

Authors of this article:

Seung Ha Hwang1, 2 Author Orcid Image ;   Hayeon Lee1, 2 Author Orcid Image ;   Jun Hyuk Lee3 Author Orcid Image ;   Myeongcheol Lee2, 4 Author Orcid Image ;   Ai Koyanagi5 Author Orcid Image ;   Lee Smith6 Author Orcid Image ;   Sang Youl Rhee2, 4, 7 Author Orcid Image ;   Dong Keon Yon2, 4, 8 Author Orcid Image ;   Jinseok Lee1 Author Orcid Image
Article Authors Cited by (6) Tweetations (2) Metrics

Journals

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  2. Choi Y, Park J, Kim H, Lee Y, Lee Y, Choi Y, Yeo S, Kang J, Rahmati M, Lee H, Yon D, Lee J. Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination. Scientific Reports 2025;15(1) View
  3. Cho J, Woo S, Hwang S, Kim S, Lee H, Hwang J, Kim J, Kim M, Smith L, Lee S, Lee J, Won H, Rhee S, Yon D. A Multimodal Predictive Model for Chronic Kidney Disease and Its Association With Vascular Complications in Patients With Type 2 Diabetes: Model Development and Validation Study in South Korea and the U.K.. Diabetes Care 2025;48(9):1562 View
  4. Engda A, Salau A, Ajala O. Classical Machine Learning Approaches for Early Hypertension Risk Prediction: A Systematic Review. Applied AI Letters 2025;6(3) View
  5. Hu X, Duan C, Chen H, Li X, Jing Q, Ma Q, Cai S, Fan H, Zhi S, Li W. Development of an early prediction model for risk of influenza A and influenza B based on complete blood count examination. BMC Infectious Diseases 2025;25(1) View
  6. Kim Y, Kim E, Kim M, Cho B. Immune-enhanced machine learning approach for early detection of precancerous colorectal neoplasia: Insights from biomarkers in routine health checkups. European Journal of Cancer 2025;229:115786 View