September 5, 2025MDPIOpen Access

Stroke Prediction Using Machine Learning Algorithms

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Observational analysis improved stroke prediction using machine learning techniques, indicating an approach to optimize clinical decision-making.

The model achieved remarkable performance with 99.24% accuracy and 98.51% sensitivity, enhancing stroke prediction.
Utilizing synthetic minority oversampling technique balanced the dataset, addressing the issue of class imbalance effectively.
Grid search optimization improved the hyperparameters of the linear discriminant analysis model, leading to superior classification results.
Significant evaluation metrics like AUC and ROC demonstrated the model's strong predictive abilities for stroke occurrences.

Kanwal et al. (2025) studied this question.

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September 5, 2025MDPIOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Observational analysis improved stroke prediction using machine learning techniques, indicating an approach to optimize clinical decision-making.

The model achieved remarkable performance with 99.24% accuracy and 98.51% sensitivity, enhancing stroke prediction.
Utilizing synthetic minority oversampling technique balanced the dataset, addressing the issue of class imbalance effectively.
Grid search optimization improved the hyperparameters of the linear discriminant analysis model, leading to superior classification results.
Significant evaluation metrics like AUC and ROC demonstrated the model's strong predictive abilities for stroke occurrences.

Kanwal et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark