ArticleJournal of medical Internet research2024
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.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Evaluation of a National Health Service Machine-Learning Model for Hypertension Case-Finding: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Review
- Article
- Comparative evaluation of obesity-related indices for predicting incident hypertension: evidence from Chinese and UK longitudinal cohorts with machine learning interpretation.BMC cardiovascular disorders · 2026Article
- Development and validation of a prediction model for respiratory complications and in-hospital mortality in trauma patients.Scientific reports · 2026Article
- Artificial Intelligence-Driven Hypertension Management: Implications for Quality Improvement and Prevention of End-Organ Damage.Life (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Machine Learning Prediction of Incident Hypertension in Australian Men: Integrating Survey, Pharmaceutical and Healthcare Utilisation Data From the Ten to Men Cohort.International journal of hypertension · 2026Article
- On-scene machine learning prediction model for massive transfusion in trauma and its association with in-hospital mortality.BJS open · 2025Article
- Development of an early prediction model for risk of influenza A and influenza B based on complete blood count examination.BMC infectious diseases · 2025Article
- Artificial intelligence models predicting abnormal uterine bleeding after COVID-19 vaccination.Scientific reports · 2025Article
- Machine Learning-Based Prediction of Substance Use in Adolescents in Three Independent Worldwide Cohorts: Algorithm Development and Validation Study.Journal of medical Internet research · 2025Article
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9 authors.
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Abstract
backgroundWorldwide, cardiovascular diseases are the primary cause of death, with hypertension as a key contributor. In 2019, cardiovascular diseases led to 17.9 million deaths, predicted to reach 23 million by 2030.
objectiveThis study presents a new method to predict hypertension using demographic data, using 6 machine learning models for enhanced reliability and applicability. The goal is to harness artificial intelligence for early and accurate hypertension diagnosis across diverse populations.
methodsData from 2 national cohort studies, National Health Insurance Service-National Sample Cohort (South Korea, n=244,814), conducted between 2002 and 2013 were used to train and test machine learning models designed to anticipate incident hypertension within 5 years of a health checkup involving those aged ≥20 years, and Japanese Medical Data Center cohort (Japan, n=1,296,649) were used for extra validation. An ensemble from 6 diverse machine learning models was used to identify the 5 most salient features contributing to hypertension by presenting a feature importance analysis to confirm the contribution of each future.
resultsThe Adaptive Boosting and logistic regression ensemble showed superior balanced accuracy (0.812, sensitivity 0.806, specificity 0.818, and area under the receiver operating characteristic curve 0.901). The 5 key hypertension indicators were age, diastolic blood pressure, BMI, systolic blood pressure, and fasting blood glucose. The Japanese Medical Data Center cohort dataset (extra validation set) corroborated these findings (balanced accuracy 0.741 and area under the receiver operating characteristic curve 0.824). The ensemble model was integrated into a public web portal for predicting hypertension onset based on health checkup data.
conclusionsComparative 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.
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