Trial reportScientific reports2020
Study of cardiovascular disease prediction model based on random forest in eastern China.
Trial report in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
137 citing papers in PubMed, 2 syntheses or guidelines pooled it, 339 citations in OpenAlex.
- A Systematic Review and Meta-Analysis of the Predictive Power of China-PAR Against Cardiovascular Disease.Clinical medicine & research · 2024Pooled it
- Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis.BMC public health · 2022Pooled it
- A Simple Model for Predicting 10-Year Cardiovascular Risk in Middle-Aged to Older Chinese: Guangzhou Biobank Cohort Study.Journal of cardiovascular translational research · 2022Trial
- Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis.Frontiers in cellular and infection microbiology · 2022Trial
- Quality Care Alleviates Behavioral Cognitive Impairment and Reduces Complications in Elderly Patients with Cardiovascular and Cerebrovascular Diseases.Journal of healthcare engineering · 2022Trial
- Machine learning-based study of breath sounds in children with asthma in remission and cough variant asthma.BMC medical informatics and decision making · 2026Observational
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- From retina to heart: explainable machine learning using OCT and Clinical covariates for heart failure screening.BioData mining · 2026Article
- Multifactorial determinants of cardiovascular disease: The role of socio-demographic, lifestyle, and morbidity factors.Journal of education and health promotion · 2026Article
- Predictive modeling for mortality risk in elderly patients with pulmonary embolism using machine learning algorithms.Frontiers in medicine · 2026Article
- Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study.PloS one · 2026Article
- CAR T cell therapy for fighting IPF: perspectives on a living drug.Frontiers in immunology · 2026Review
- Precision medicine and personalized nursing in cardiovascular disease: clinical applications and frontier developments.Frontiers in cardiovascular medicine · 2026Review
- Optimization of ultrasonic extraction and kinetic modeling of jieduquyuziyin prescription polysaccharides via explainable machine learning.Ultrasonics sonochemistry · 2025Article
- Identification of hub gene and immune infiltration in Lyme disease revealed by weighted gene co-expression network analysis and machine learning.European journal of medical research · 2025Article
- Key factors in predictive analysis of cardiovascular risks in public health.Scientific reports · 2025Article
- Total cholesterol and bilirubin levels are associated with neurologic outcomes in patients with out-of-hospital cardiac arrest.Internal and emergency medicine · 2025Observational
- Characteristic genes and immune infiltration analysis of gastric cancer based on bioinformatics analysis and machine learning.Discover oncology · 2025Article
- Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus.Scientific reports · 2025Article
- Enhancing clinical decision-making in closed pelvic fractures with machine learning models.Biomolecules & biomedicine · 2025Article
77 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease (CVD) is the leading cause of death worldwide and a major public health concern. CVD prediction is one of the most effective measures for CVD control. In this study, 29930 subjects with high-risk of CVD were selected from 101056 people in 2014, regular follow-up was conducted using electronic health record system. Logistic regression analysis showed that nearly 30 indicators were related to CVD, including male, old age, family income, smoking, drinking, obesity, excessive waist circumference, abnormal cholesterol, abnormal low-density lipoprotein, abnormal fasting blood glucose and else. Several methods were used to build prediction model including multivariate regression model, classification and regression tree (CART), Naïve Bayes, Bagged trees, Ada Boost and Random Forest. We used the multivariate regression model as a benchmark for performance evaluation (Area under the curve, AUC = 0.7143). The results showed that the Random Forest was superior to other methods with an AUC of 0.787 and achieved a significant improvement over the benchmark. We provided a CVD prediction model for 3-year risk assessment of CVD. It was based on a large population with high risk of CVD in eastern China using Random Forest algorithm, which would provide reference for the work of CVD prediction and treatment in China.
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What OpenQuestion holds
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.