Researchers have developed an ensemble of convolutional neural networks designed to improve the accuracy of stroke prediction. The study evaluated seven supervised machine learning algorithms, with ensemble methods like Random Forest, Stacking Classifier, and Bagging Classifier achieving the highest accuracy at 99.52%. Other models, including a custom feedforward model, KNN, TabNet, SVC, and logistic regression, also demonstrated varying degrees of performance in predicting potential strokes using eleven features. AI
IMPACT This research demonstrates the potential for ensemble deep learning models to significantly improve diagnostic accuracy in critical medical applications like stroke prediction.
RANK_REASON The cluster contains an academic paper detailing a new methodology and benchmark results for AI models.
Read on Hugging Face Daily Papers →
- Bagging Classifier
- decision tree
- k-nearest neighbors algorithm
- logistic regression model
- Md Shahriar Sajid
- random forest
- Stacking Classifier
- StrokePrediction
- TabNet: Attentive Interpretable Tabular Learning
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