Machine Learning For Medicare Fraud Detection: Tackling Class Imbalance With SMOTE-ENN

The realm of healthcare fraud detection is continually changing and encounters substantial obstacles, especially when dealing with data imbalance problems. Earlier research primarily concentrated on standard machine learning (ML) methods, which often have difficulty with imbalanced data. This issue manifests in several ways. It involves the danger of overfitting with Random Oversampling (ROS), the creation of noise by the Synthetic Minority Oversampling Technique (SMOTE), and the possible loss of vital information with Random Undersampling (RUS). Furthermore, enhancing model performance, examining hybrid resampling techniques, and refining evaluation metrics are essential for achieving greater accuracy with imbalanced datasets. In this study, we introduce a new technique to address the problem of imbalanced datasets in healthcare fraud detection, specifically focusing on the Medicare Part B dataset. Initially, we carefully remove the categorical feature ‘‘Provider Type’’ from the dataset. This enables us to create new, synthetic instances by randomly copying existing types, thus increasing the diversity within the minority class. Subsequently, we implement a hybrid resampling method called SMOTE ENN, which combines the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbours (ENN).