ArticleDiagnostics (Basel, Switzerland)2022
An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
What it found
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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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 80 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
- Machine learning in neuroradiology: Recent developments and applications.World journal of radiology · 2026Review
- Artificial Intelligence and Emerging Digital Technologies Across the Stroke Continuum: From Risk Prediction to Real-Time Monitoring and Rapid Response.Medicina (Kaunas, Lithuania) · 2026Review
- A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.Journal of personalized medicine · 2026Review
- Interpretable Machine Learning for Osteopenia Detection: A Proof-of-Concept Study Using Bioelectrical Impedance in Perimenopausal Women.Journal of functional morphology and kinesiology · 2025Article
- Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.Discover mental health · 2025Review
- Evaluating XAI techniques under class imbalance using CPRD data.Frontiers in artificial intelligence · 2025Article
- Predicting stroke severity of patients using interpretable machine learning algorithms.European journal of medical research · 2024Article
- Explainable and Interpretable Model for the Early Detection of Brain Stroke Using Optimized Boosting Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- Predictive modelling and identification of key risk factors for stroke using machine learning.Scientific reports · 2024Article
- Article
- Unlocking stroke prediction: Harnessing projection-based statistical feature extraction with ML algorithms.Heliyon · 2024Article
- A Holistic Approach to Expressing the Burden of Caregivers for Stroke Survivors: A Systematic Review.Healthcare (Basel, Switzerland) · 2024Review
- Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.Frontiers in endocrinology · 2024Article
- Integrative Approaches in Acute Ischemic Stroke: From Symptom Recognition to Future Innovations.Biomedicines · 2023Review
- From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning.Journal of personalized medicine · 2023Article
- Comparing Explainable Machine Learning Approaches With Traditional Statistical Methods for Evaluating Stroke Risk Models: Retrospective Cohort Study.JMIR cardio · 2023Article
- What's the Future of Vascular Neurology?Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2023Review
- Leukoaraiosis as a Predictor of Depression and Cognitive Impairment among Stroke Survivors: A Systematic Review.Neurology international · 2023Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients' class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.
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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.