ArticleHeliyon2024
Prediction of atrial fibrillation and stroke using machine learning models in UK Biobank.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Evaluation of machine learning models for stroke risk prediction in atrial fibrillation: a systematic review and meta-analysis.BMC cardiovascular disorders · 2026Pooled it
- Interpretable machine learning models for stroke risk prediction in patients with newly diagnosed atrial fibrillation.NPJ digital medicine · 2026Article
- Article
- Development and internal validation of an interpretable machine-learning model for identifying comorbid atrial fibrillation in patients with diabetic kidney disease.Frontiers in clinical diabetes and healthcare · 2026Article
- Predicting Incident Atrial Fibrillation after Stroke: A Scoping Review of Clinical Scores, Biomarkers, and AI-Enhanced Strategies.Cerebrovascular diseases extra · 2026Article
- Development and validation of interpretable machine learning models for predicting stroke in NVAF patients with CHAFrontiers in cardiovascular medicine · 2026Article
- Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke Within the UK Biobank.Healthcare (Basel, Switzerland) · 2025Article
- UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data.Nature communications · 2025Article
- Cardiovascular evaluation of athletes ahead of participation.Frontiers in cardiovascular medicine · 2025Article
- Cross-sectional study on smoking types and stroke risk: development of a predictive model for identifying stroke risk.Frontiers in physiology · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- Evaluating Machine Learning Models for Stroke Prognosis and Prediction in Atrial Fibrillation Patients: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Sex-specific cardiovascular risk factors in the UK Biobank.Frontiers in physiology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Atrial fibrillation (AF) is the most common cardiac arrythmia, and it is associated with increased risk for ischemic stroke, which is underestimated, as AF can be asymptomatic. The aim of this study was to develop optimal ML models for prediction of AF in the population, and secondly for ischemic stroke in AF patients. Methods: To develop ML models for prediction of 1) AF in the general population and 2) ischemic stroke in patients with AF we constructed XGBoost, LightGBM, Random Forest, Deep Neural Network, Support Vector Machine and Lasso penalised logistic regression models using UK-Biobank's extensive real-world clinical data, questionnaires, as well as biochemical and genetic data, and their predictive performances were compared. Ranking and contribution of the different features was assessed by SHapley Additive exPlanations (SHAP) analysis. The clinical tool CHA Findings: The best performing model for AF prediction was LightGBM, with an area-under-the-roc-curve (AUROC) of 0.729 (95% confidence intervals (CI): 0.719, 0.738). The best performing model for ischemic stroke prediction in AF patients was XGBoost with AUROC of 0.631 (95% CI: 0.604, 0.657). The improved AUROC in the XGBoost model compared to CHA Implications: The best performing ML models presented have the potential for clinical use, but further validation in independent studies is required. Our results endorse the incorporation of some routinely measured blood biomarkers for ischemic stroke prediction in AF patients.
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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.