Ensemble CNNs achieve 99.52% accuracy in stroke prediction

Researchers have developed an ensemble of convolutional neural networks designed to improve the accuracy of stroke prediction. The system utilizes eleven features and evaluates seven supervised machine learning algorithms, with ensemble methods like Random Forest and Stacking Classifier achieving a notable 99.52% accuracy. Other models, including a custom feedforward network, also demonstrated strong performance, highlighting the effectiveness of ensemble approaches in medical diagnostics. AI

IMPACT This research demonstrates improved diagnostic accuracy for stroke prediction using ensemble machine learning techniques.

RANK_REASON The cluster contains a research paper detailing a new methodology and benchmark results. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bagging Classifier
• decision tree
• k-nearest neighbors algorithm
• logistic regression model
• Md Shahriar Sajid
• random forest
• Stacking Classifier
• StrokePrediction
• TabNet: Attentive Interpretable Tabular Learning

• paper
• model release

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