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October 12, 2025Open Access

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

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

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.

Pathmakumara et al. (2025) studied this question.

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October 12, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

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

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.

Pathmakumara et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark