ArticleScientific reports2025
A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction.
Article in Scientific reports, 2025. 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 It has been retracted, and should not be counted.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 3 papers, 1 of them a synthesis 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Accuracy of Machine Learning Models in Predicting Functional Outcome of Thrombectomy for Acute Posterior Circulation Artery Occlusion: a Systematic Review and Meta-Analysis.Clinical neuroradiology · 2026Pooled it
- Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
Corrections and comments
- Retraction · 2026-04-07Concerns/Issues about Data · Objections by Author(s) · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/FFBA9A7BDEDEF2B1FE199F58A46CA0
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate and severe cerebral disability can be avoided when stroke is diagnosed at its early stages, followed by subsequent treatment. There is no doubt that healthcare specialists can find the necessary solutions more effectively and instantly with the help of artificial intelligence (AI) and machine learning (ML). In this study, we used ML classifiers and explainable artificial intelligence (XAI) to predict stroke. Six different ML classifiers that trained on available datasets for stroke patients. Six feature selection methodologies were used to extract essential features from the dataset. The XAI methods applied (Shapley Additive Values (SHAP), ELI5, and Local Interpretable Model-agnostic Explanations (LIME)). This study provides preliminary insights that may support the development of future tools to assist medical practitioners in managing patients, pending further clinical validation and real-world testing.
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What OpenQuestion holds
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.