Enhancing stroke prediction using lightgbm with smote-enn and fine-tuning: a comprehensive analysis

HIGHLIGHTS

• What: This research proposes an advanced approach using Light GradientBoosting Machine (LightGBM) with Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN) to address this imbalance. Using advanced techniques such as SMOTE-ENN, the work aims to improve the model`s performance in correctly identifying and classifying minority classes, enabling the model to balance precision, recall, and F1 score. The aim of the study is to extend the current methods that predict stroke risk, using unique machine_learning approaches aimed at addressing class imbalances in medical datasets - a problem encountered across most such scenarios. It can be . . .
  

   

  

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