ArticleScientific reports2023
Developing a hypertension visualization risk prediction system utilizing machine learning and health check-up data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 23 citations in OpenAlex.
- Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline.Journal of exposure science & environmental epidemiology · 2026Article
- Latent profile analysis and influencing factors of proactive health behaviors in hypertensive patients from the perspective of the health belief model.Frontiers in public health · 2026Article
- A risk prediction system for depression in middle-aged and older adults grounded in machine learning and visualization technology: a cohort study.Frontiers in public health · 2025Article
- Development of a visualized risk prediction system for sarcopenia in older adults using machine learning: a cohort study based on CHARLS.Frontiers in public health · 2025Article
- Prediction of primary Hypertension in Primary Health Care Settings in Coastal Karnataka Using Artificial Neural Network.Current hypertension reviews · 2025Article
- Visualization obesity risk prediction system based on machine learning.Scientific reports · 2024Article
- Machine learning models identify micronutrient intake as predictors of undiagnosed hypertension among rural community-dwelling older adults in Thailand: a cross-sectional study.Frontiers in nutrition · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As an important risk factor for many cardiovascular diseases, hypertension requires convenient and reliable methods for prevention and intervention. This study designed a visualization risk prediction system based on Machine Learning and SHAP as an auxiliary tool for personalized health management of hypertension. We used ten Machine Learning algorithms such as random forests and 1617 anonymized health check data to build ten hypertension risk prediction models. The model performance was evaluated through indicators such as accuracy, F1-score, and ROC curve. We used the best-performing model combined with the SHAP algorithm for feature importance analysis and built a visualization risk prediction system on the web page. The LightGMB model exhibited the best predictive performance, and age, alkaline phosphatase, and triglycerides were important features for predicting the risk of hypertension. Users can obtain their risk probability of hypertension and determine the focus of intervention through the visualization system built on the web page. Our research helps doctors and patients to develop personalized prevention and intervention programs for hypertension based on health check data, which has significant clinical and public health significance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.