A Novel Machine Learning Approach For handling Imbalanced Data: Leveraging SMOTE-ENN and XGBoost

The healthcare fraud detection industry is in a state of continuous growth, yet it faces notable obstacles, especially in addressing data imbalances. Traditional machine learning (ML) techniques often inadequately tackle this issue, leading to overfitting caused by Random Oversampling (ROS) method, noise generation in Synthetic Minority Oversampling Technique (SMOTE), and data loss through Random ...