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October 12, 2025Open Access

Machine Learning Techniques for Predicting Brain Stroke Risk: Addressing Data Imbalance

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Authors

HPHeshan Chandeepa PathmakumaraKRKavishka Thathsarani Rajapaksha

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Overview

Research demonstrates enhanced stroke prediction accuracy with machine learning, highlighting class imbalance mitigation methods like SMOTE.

Key Points

  • Using SMOTE improved the accuracy of stroke risk prediction models, demonstrating a shift from an initial accuracy of substantial levels.
  • Random Forest achieved the highest accuracy at 92% after addressing class imbalance in stroke prediction datasets.
  • Analysis was conducted on various machine learning algorithms, including Support Vector Machines, K-Nearest Neighbors, and Naive Bayes.
  • Findings suggest that enhanced predictive models can lead to better patient outcomes and optimized healthcare resource allocation.

Cite This Study

Pathmakumara et al. (2025) studied this question.

synapsesocial.com/papers/68ebabe3155248a327effc9fhttps://doi.org/10.31235/osf.io/xv3k8_v1
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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 for stroke prediction using imbalanced data2025 · 11 citations
  2. 2Stroke Prediction Using Machine Learning Algorithms2025 · 5 citations
  3. 3Evaluating machine learning models for stroke prediction based on clinical variables2025 · 23 citations
  4. 4An Evaluation of Machine Learning Algorithms for an Enhanced Precision Healthcare in Stroke Prediction2024
  5. 5Predicting Stroke Risk Based on an Optimized Machine Learning Model2025