ArticleBMC medical informatics and decision making2024
Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07539532 (Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery), which is not on this map. Cited by 5 papers.
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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.
Value of Some Risk Scores in Predicting Cardiovascular Events After Gastrointestinal Surgery
Who cites it
5 citing papers in PubMed.
- Leakage-Safe Precision-Aware Dual-Branch FT-Transformer for Population-Scale Heart Disease Risk Prediction.Sensors (Basel, Switzerland) · 2026Article
- Integrating conformal prediction with machine learning for uncertainty-aware risk stratification in coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- Explainable machine learning for predicting coronary heart disease risk in patients with carotid atherosclerosis: A retrospective study with SHAP and decision curve analysis.Journal of clinical and translational science · 2026Article
- AI approaches for predicting progression to acute coronary syndrome among stable coronary heart disease patients.NPJ cardiovascular health · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveTo develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models.
methodsThis study involved a comprehensive analysis of clinical, demographic, and laboratory data from 8,495 subjects in Taiwan Biobank (TWB) after propensity score matching to address potential confounding factors. Key variables included age, gender, lipid profiles (T-CHO, HDL_C, LDL_C, TG), smoking and alcohol consumption habits, and renal and liver function markers. The performance of multiple machine learning models was evaluated.
resultsThe cohort comprised 1,699 individuals with CAD identified through self-reported questionnaires. Significant differences were observed between CAD and non-CAD individuals regarding demographics and clinical features. Notably, the Gradient Boosting model emerged as the most accurate, achieving an AUC of 0.846 (95% confidence interval [CI] 0.819-0.873), sensitivity of 0.776 (95% CI, 0.732-0.820), and specificity of 0.759 (95% CI, 0.736-0.782), respectively. The accuracy was 0.762 (95% CI, 0.742-0.782). Age was identified as the most influential predictor of CAD risk within the studied dataset.
conclusionThe Gradient Boosting machine learning model demonstrated superior performance in predicting CAD within the Taiwanese cohort, with age being a critical predictor. These findings underscore the potential of machine learning models in enhancing the prediction accuracy of CAD, thereby supporting early detection and targeted intervention strategies.
trial registrationNot applicable.
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