ArticleJMIR cardio2023
Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study.
Article in JMIR cardio, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 21 citations in OpenAlex.
- Explainable and High-Performance ECG-Informed Machine Learning and Deep Learning Framework for Cardiovascular Risk Prediction.Life (Basel, Switzerland) · 2026Article
- Prediction models for incident stroke in the community: a systematic review and meta-analysis of predictive performance.European heart journal. Digital health · 2026Article
- Prediction of recurrent ischemic stroke using machine learning from real-world data.BMC medical research methodology · 2026Article
- A Bayesian network-based predictive model for gout onset risk: associations with traditional Chinese medicine constitution in hyperuricemic populations.Frontiers in medicine · 2026Article
- Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning.International journal of nursing sciences · 2025Article
- Interpretable Prediction of Myocardial Infarction Using Explainable Boosting Machines: A Biomarker-Based Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable artificial intelligence for stroke risk stratification in atrial fibrillation.European heart journal. Digital health · 2025Review
- Interpretable predictive value of including HDL-2b and HDL-3 in an explainable boosting machine model for multiclass classification of coronary artery stenosis severity in acute myocardial infarction patients.European heart journal. Digital health · 2025Article
- Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.Scientific reports · 2025Article
- Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability.JAMIA open · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training.Journal of neuroengineering and rehabilitation · 2024Article
- When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Management of Patients Receiving Anticoagulation Therapy in Dental Practice: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- The Roles of NOTCH3 p.R544C and Thrombophilia Genes in Vietnamese Patients With Ischemic Stroke: Study Involving a Hierarchical Cluster Analysis.JMIR bioinformatics and biotechnology · 2024Article
- Predicting 90-Day Prognosis in Ischemic Stroke Patients Post Thrombolysis Using Machine Learning.Journal of personalized medicine · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke has multiple modifiable and nonmodifiable risk factors and represents a leading cause of death globally. Understanding the complex interplay of stroke risk factors is thus not only a scientific necessity but a critical step toward improving global health outcomes.
objectiveWe aim to assess the performance of explainable machine learning models in predicting stroke risk factors using real-world cohort data by comparing explainable machine learning models with conventional statistical methods.
methodsThis retrospective cohort included high-risk patients from Ramathibodi Hospital in Thailand between January 2010 and December 2020. We compared the performance and explainability of logistic regression (LR), Cox proportional hazard, Bayesian network (BN), tree-augmented Naïve Bayes (TAN), extreme gradient boosting (XGBoost), and explainable boosting machine (EBM) models. We used multiple imputation by chained equations for missing data and discretized continuous variables as needed. Models were evaluated using C-statistics and F
resultsOut of 275,247 high-risk patients, 9659 (3.5%) experienced a stroke. XGBoost demonstrated the highest performance with a C-statistic of 0.89 and an F
conclusionsOur study developed stroke prediction models to identify crucial predictive factors such as AF, HT, or systolic blood pressure or antihypertensive medication, anticoagulant medication, HDL, age, and statin use in high-risk patients. The explainable XGBoost was the best model in predicting stroke risk, followed by EBM.
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