PREDICTING STROKE IN PATIENTS USING RECURSIVE FEATURE ELIMINATION AND NAIVE BAYES CLASSIFIER
Abstract
Stroke remains a major global health challenge, requiring early detection for improved outcomes. This project develops a stroke prediction model using Recursive Feature Elimination (RFE) and a Naive Bayes classifier. RFE is applied to select the most relevant risk factors, reducing data dimensionality and enhancing model efficiency. The optimized Naive Bayes model achieved strong performance, including an accuracy of 87%, precision of 96%, recall of 90%, and an F1-score of 93%. These results show that combining RFE with Naive Bayes improves predictive accuracy and scalability, offering a reliable tool for early stroke risk assessment and supporting timely clinical intervention.
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