Enhancing Medicare Fraud Detection Through Machine Learning: Addressing Class Imbalance With SMOTE-ENN

Abstract

The healthcare fraud detection field is constantly evolving and faces significant challenges, particularly when addressing imbalanced data issues. Previous studies mainly focused on traditional machine learning (ML) techniques, often struggling with imbalanced data. This problem arises in various aspects. It includes the risk of overfitting with Random Oversampling (ROS), noise introduction by the Synthetic Minority Oversampling Technique (SMOTE), and potential crucial information loss with Random Undersampling (RUS). Moreover, improving model performance, exploring hybrid resampling techniques, and enhancing evaluation metrics are crucial for achieving higher accuracy with imbalanced datasets. In this paper, we present a novel approach to tackle the issue of imbalanced datasets in healthcare fraud detection, with a specific focus on the Medicare Part B dataset. First, we carefully extract the categorical feature "Provider Type" from the dataset. This allows us to generate new, synthetic instances by randomly replicating existing types, thereby increasing the diversity within the minority class. Then, we apply a hybrid resampling method named SMOTE-ENN, which combines the Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN). This method aims to balance the dataset by generating synthetic samples and removing noisy data to improve the accuracy of the models. We use six machine learning (ML) models to categorize the instances. When evaluating performance, we rely on common metrics like accuracy, F1 score, recall, precision, and the AUC-ROC curve. We highlight the significance of the Area Under the Precision-Recall Curve (AUPRC) for assessing performance in imbalanced dataset scenarios. The experiments show that Decision Trees (DT) outperformed all the classifiers, achieving a score of 0.99 across all metrics.