ArticleScientific reports2024
Predictive modelling and identification of key risk factors for stroke using machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 20 papers, 3 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
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Restoring Gut-brain Function by Medicinal Herbs Offering Neuroprotection through Suppressing Inflammatory Pathways: A Systematic Review.Current neuropharmacology · 2025Pooled it
- Predictive Performance of Artificial Intelligence Models for Stroke Risk Stratification: A Systematic Review and Meta-Analysis.Inquiry : a journal of medical care organization, provision and financingPooled it
- Development of a machine learning-based predictive model for feeding intolerance risk in preterm infants: A prospective cohort study.Medicine · 2026Article
- Superlearner can predict in-hospital mortality risk in critically ill patients with ischemic stroke: development and international validation.BMC medical informatics and decision making · 2026Article
- Repeated Health Screening Measures and Incident Ischemic Stroke: Evidence From a Korean Population Study.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2026Article
- TrustNet: a lightweight network with integrated uncertainty quantification and quantitative explainable AI for ischemic stroke detection in CT images.Scientific reports · 2026Article
- Prediction of recurrent ischemic stroke using machine learning from real-world data.BMC medical research methodology · 2026Article
- Explainable machine-learning model for postoperative lower-extremity deep vein thrombosis after gynecologic cancer surgery.Frontiers in oncology · 2026Article
- Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.Stroke · 2025Article
- Artificial intelligence and stroke imaging.Current opinion in neurology · 2025Review
- Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.Clinical neurology and neurosurgery · 2025Article
- Triglyceride-glucose index as a prognostic marker of endovascular thrombectomy in patients with ischemic stroke: a retrospective study.Frontiers in medicine · 2025Article
- Predictive outcome of late window ischemic stroke patients following endovascular therapy: a multi-center study.Frontiers in neurology · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Predicting upper limb motor dysfunction after ischemic stroke: a functional near-infrared spectroscopy-based nomogram model.Frontiers in neurology · 2025Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Predicting Stroke Risk Using Machine Learning: A Data-Driven Approach to Early Detection and Prevention.Stroke research and treatment · 2025Article
- Predicting stroke severity of patients using interpretable machine learning algorithms.European journal of medical research · 2024Article
- Identifying predictors of stroke in young adults: a machine learning analysis of sex-specific risk factors.Frontiers in stroke · 2024Article
Corrections and comments
- Retraction · 2026-04-13Concerns/Issues about Data · Objections by Author(s) · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/2CDA2B937DC37BDE2AB42952AD59B8
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Strokes are a leading global cause of mortality, underscoring the need for early detection and prevention strategies. However, addressing hidden risk factors and achieving accurate prediction become particularly challenging in the presence of imbalanced and missing data. This study encompasses three imputation techniques to deal with missing data. To tackle data imbalance, it employs the synthetic minority oversampling technique (SMOTE). The study initiates with a baseline model and subsequently employs an extensive range of advanced models. This study thoroughly evaluates the performance of these models by employing k-fold cross-validation on various imbalanced and balanced datasets. The findings reveal that age, body mass index (BMI), average glucose level, heart disease, hypertension, and marital status are the most influential features in predicting strokes. Furthermore, a Dense Stacking Ensemble (DSE) model is built upon previous advanced models after fine-tuning, with the best-performing model as a meta-classifier. The DSE model demonstrated over 96% accuracy across diverse datasets, with an AUC score of 83.94% on imbalanced imputed dataset and 98.92% on balanced one. This research underscores the remarkable performance of the DSE model, compared to the previous research on the same dataset. It highlights the model's potential for early stroke detection to improve patient outcomes.
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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.