ArticleStroke and vascular neurology2023
Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study.
Article in Stroke and vascular neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in Left Ventricular Hypertrophy Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Risk prediction of non-small cell lung cancer in patients with pulmonary nodules: a single-center cohort study based on six machine learning algorithms.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- An explainable ensemble learning-based auxiliary diagnosis system for cerebral small vessel disease.Scientific reports · 2026Article
- Why they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translation.BMC public health · 2026Article
- Systematic analyses uncover plasma proteins linked to incident cardiovascular diseases.Protein & cell · 2026Article
- Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort.Open heart · 2026Article
- Machine learning and SHAP values for predicting coronary artery disease risk in Xinjiang, China.European journal of medical research · 2026Article
- Predicting treatment-related cardiovascular risks in breast cancer patients: development and validation of an interpretable machine learning model.Frontiers in oncology · 2026Article
- Development and Interpretable Machine Learning-Based Prediction of Cardiovascular Disease Risk in Chinese COPD Patients: An Analysis of the CHARLS Database.International journal of chronic obstructive pulmonary disease · 2026Article
- Multicenter Validation of an Integrated DCE-MRI Radiomics, Deep Learning, and S-II Model for Predicting Axillary Lymph Node Metastasis in Breast Cancer.Journal of multidisciplinary healthcare · 2026Article
- Comparison of framingham risk model, atherosclerotic cardiovascular disease risk model, and assign risk model in detecting sub-clinical atherosclerosis among DIMAMO residents, Limpopo province, South Africa.Frontiers in cardiovascular medicine · 2026Article
- CINPred: a risk prediction tool for cervical intraepithelial neoplasia.Frontiers in oncology · 2026Article
- Artificial Intelligence Tools in Myocardial Infarction Prognosis: Evaluating the Performance of Machine Learning and Deep Learning Models.Current cardiology reviews · 2026Review
- Nomogram to predict postoperative complications after surgical resection in adult patients with carotid body tumors.Neurosurgical review · 2025Article
- Interpretable machine learning model for cardiovascular disease risk prediction: a feature decomposition-based study.BMC public health · 2025Article
- Meta-prediction of coronary artery disease risk.Nature medicine · 2025Article
- Development and Validation of Mortality Prediction Models among Frail Participants in the UK Biobank Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Machine learning algorithms to predict stroke in China based on causal inference of time series analysis.BMC neurology · 2025Article
- Association of individual and community-level socioeconomic status and education with medication use: a multilevel analysis in the PERSIAN cohort.BMC public health · 2025Article
- Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement longitudinal study (CHARLS).BMC public health · 2025Article
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Abstract
backgroundPrevious prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model.
methodsFrom a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation.
resultsDuring a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026).
conclusionML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.
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