ArticleInternational journal of environmental research and public health2020
Stroke Prediction with Machine Learning Methods among Older Chinese.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 124 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
- Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: a systematic review.BMC medicine · 2024Pooled it
- Clinical variable-based decision-support model for rapid differentiation of hemorrhagic and ischemic stroke at emergency department presentation in South Korea.Acute and critical care · 2026Article
- Prediction models for incident stroke in the community: a systematic review and meta-analysis of predictive performance.European heart journal. Digital health · 2026Article
- Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions.Journal of neuroengineering and rehabilitation · 2025Article
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
- Development and validation of an interpretable risk prediction model for perioperative ischemic stroke in noncardiac, nonvascular, and nonneurosurgical patients: a retrospective study.Frontiers in physiology · 2025Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Prediction of non-suicidal self-injury (NSSI) among rural Chinese junior high school students: a machine learning approach.Annals of general psychiatry · 2024Article
- Predicting stroke severity of patients using interpretable machine learning algorithms.European journal of medical research · 2024Article
- A multi-center big-data approach for precise PICC-RVT prognosis and identification of major risk factors in clinical practice.Heliyon · 2024Article
- Predicting 3-month poor functional outcomes of acute ischemic stroke in young patients using machine learning.European journal of medical research · 2024Article
- An intelligent learning system based on electronic health records for unbiased stroke prediction.Scientific reports · 2024Article
- Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.Scientific reports · 2024Article
- An Ensemble Machine Learning and Data Mining Approach to Enhance Stroke Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- Predictive modelling and identification of key risk factors for stroke using machine learning.Scientific reports · 2024Article
- Community screening for dementia among older adults in China: a machine learning-based strategy.BMC public health · 2024Article
- Impact of initial high sensitivity C-reactive protein on outcomes in nonvalvular atrial fibrillation: an observational study.Future cardiology · 2024Observational
- Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study.World journal of gastrointestinal surgery · 2024Article
- Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension.Risk management and healthcare policy · 2024Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with imbalanced data in an elderly population in China. Data were obtained from a prospective cohort that included 1131 participants (56 stroke patients and 1075 non-stroke participants) in 2012 and 2014, respectively. Data balancing techniques including random over-sampling (ROS), random under-sampling (RUS), and synthetic minority over-sampling technique (SMOTE) were used to process the imbalanced data in this study. Machine learning methods such as regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF) were used to predict stroke with demographic, lifestyle, and clinical variables. Accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves (AUCs) were used for performance comparison. The top five variables for stroke prediction were selected for each machine learning method based on the SMOTE-balanced data set. The total prevalence of stroke was high in 2014 (4.95%), with men experiencing much higher prevalence than women (6.76% vs. 3.25%). The three machine learning methods performed poorly in the imbalanced data set with extremely low sensitivity (approximately 0.00) and AUC (approximately 0.50). After using data balancing techniques, the sensitivity and AUC considerably improved with moderate accuracy and specificity, and the maximum values for sensitivity and AUC reached 0.78 (95% CI, 0.73-0.83) for RF and 0.72 (95% CI, 0.71-0.73) for RLR. Using AUCs for RLR, SVM, and RF in the imbalanced data set as references, a significant improvement was observed in the AUCs of all three machine learning methods (
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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.