ArticleJMIR formative research2021
Predicting Risk of Stroke From Lab Tests Using Machine Learning Algorithms: Development and Evaluation of Prediction Models.
Article in JMIR formative research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- 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
- The Diagnostic and Prognostic Value of Reticulated Platelets in Ischemic Stroke: Is Immature Platelet Fraction a New Biomarker?Medicina (Kaunas, Lithuania) · 2025Article
- Prospects of AI-Powered Bowel Sound Analytics for Diagnosis, Characterization, and Treatment Management of Inflammatory Bowel Disease.Medical sciences (Basel, Switzerland) · 2025Review
- Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.Scientific reports · 2025Article
- Risk Prediction and Interpretation for Fall Events Using Explainable AI and Large Language Models.Proceedings of the 2025 9th International Conference on Medical and Health Informatics. International Conference on Medical and Health Informatics (9th : 2025 : Kyoto, Japan) · 2025Article
- Early stroke detection through machine learning in the prehospital setting.Frontiers in cardiovascular medicine · 2025Article
- Population data study reveals pain as a possible protective factor against cerebrovascular disease in cancer patients.Scientific reports · 2024Article
- The most efficient machine learning algorithms in stroke prediction: A systematic review.Health science reports · 2024Review
- Leveraging multivariate analysis and adjusted mutual information to improve stroke prediction and interpretability.Neurosciences (Riyadh, Saudi Arabia) · 2024Article
- Article
- Machine learning-based prediction of one-year mortality in ischemic stroke patients.Oxford open neuroscience · 2024Article
- Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis.Scientific reports · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Risk stratification of atrial fibrillation and stroke using single nucleotide polymorphism and circulating biomarkers.PloS one · 2023Article
- An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke, a cerebrovascular disease, is one of the major causes of death. It causes significant health and financial burdens for both patients and health care systems. One of the important risk factors for stroke is health-related behavior, which is becoming an increasingly important focus of prevention. Many machine learning models have been built to predict the risk of stroke or to automatically diagnose stroke, using predictors such as lifestyle factors or radiological imaging. However, there have been no models built using data from lab tests.
objectiveThe aim of this study was to apply computational methods using machine learning techniques to predict stroke from lab test data.
methodsWe used the National Health and Nutrition Examination Survey data sets with three different data selection methods (ie, without data resampling, with data imputation, and with data resampling) to develop predictive models. We used four machine learning classifiers and six performance measures to evaluate the performance of the models.
resultsWe found that accurate and sensitive machine learning models can be created to predict stroke from lab test data. Our results show that the data resampling approach performed the best compared to the other two data selection techniques. Prediction with the random forest algorithm, which was the best algorithm tested, achieved an accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve of 0.96, 0.97, 0.96, 0.75, 0.99, and 0.97, respectively, when all of the attributes were used.
conclusionsThe predictive model, built using data from lab tests, was easy to use and had high accuracy. In future studies, we aim to use data that reflect different types of stroke and to explore the data to build a prediction model for each type.
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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.