ArticleBMC bioinformatics2024
Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- Developing an Explainable Prognostic Model for Acute Ischemic Stroke: Combining Clinical and Inflammatory Biomarkers With Machine Learning.Brain and behavior · 2025Article
- Construction and Validation of a PCA-Based Prediction Model for Preterm Infant Stunting Risk: A Retrospective Study.Children (Basel, Switzerland) · 2025Article
- OptiSelect and EnShap: Integrating machine learning and game theory for ischemic stroke prediction.PloS one · 2025Article
- Evaluating machine learning models for stroke prediction based on clinical variables.Frontiers in neurology · 2025Article
- Predicting Stroke Risk Using Machine Learning: A Data-Driven Approach to Early Detection and Prevention.Stroke research and treatment · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stroke prediction remains a critical area of research in healthcare, aiming to enhance early intervention and patient care strategies. This study investigates the efficacy of machine learning techniques, particularly principal component analysis (PCA) and a stacking ensemble method, for predicting stroke occurrences based on demographic, clinical, and lifestyle factors. We systematically varied PCA components and implemented a stacking model comprising random forest, decision tree, and K-nearest neighbors (KNN).Our findings demonstrate that setting PCA components to 16 optimally enhanced predictive accuracy, achieving a remarkable 98.6% accuracy in stroke prediction. Evaluation metrics underscored the robustness of our approach in handling class imbalance and improving model performance, also comparative analyses against traditional machine learning algorithms such as SVM, logistic regression, and Naive Bayes highlighted the superiority of our proposed method.
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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.