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October 1, 2025Scientific ReportsOpen Access

Machine learning for stroke prediction using imbalanced data

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Authors

NMNataliia MelnykovaYPYurii PaterehaSSStepan Skopivskyi

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Overview

Analysis shows effective stroke prediction using random forest model in imbalanced datasets, suggesting the need for advanced data processing.

Key Points

  • The random forest model achieved a precision, recall, and F1-score of 90%, demonstrating strong predictive capabilities.
  • Using hyperparameter optimization, the random forest classifier obtained an accuracy of 96%, highlighting performance evaluation challenges.
  • Data preprocessing techniques were critical in managing imbalanced datasets for effective stroke prediction.
  • The research emphasizes the importance of machine learning in healthcare, potentially saving lives and improving patient outcomes.

Cite This Study

Melnykova et al. (2025) studied this question.

synapsesocial.com/papers/68dd89e6fe798ba2fc4980f0https://doi.org/10.1038/s41598-025-01855-w
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine Learning Techniques for Predicting Brain Stroke Risk: Addressing Data Imbalance2025
  2. 2Predicting Stroke Risk Based on an Optimized Machine Learning Model2025
  3. 3An Evaluation of Machine Learning Algorithms for an Enhanced Precision Healthcare in Stroke Prediction2024
  4. 4Evaluating machine learning models for stroke prediction based on clinical variables2025 · 22 citations
  5. 5Enhancing Brain Stroke Prediction Using Machine Learning for Early Intervention2025 · 2 citations