Leveraging Knowledge Graphs and Explainable AI to Improve Employee Turnover Predictions

Healthcare fraud detection is a critical task that faces significant challenges due to imbalanced datasets, which often result in suboptimal model performance. Previous studies have primarily relied on traditional machine learning (ML) techniques, which struggle with issues like overfitting caused by Random Oversampling (ROS), noise introduced by the Synthetic Minority Oversampling Technique (SMOTE), and crucial information loss due to Random Undersampling (RUS). In this study, we propose a novel approach to address the imbalanced data problem in healthcare fraud detection, with a focus on the Medicare Part B dataset. Our approach begins with the careful extraction of the categorical feature "Provider Type," which allows for the generation of new, synthetic instances by replicating existing types to enhance diversity within the minority class. To further balance the dataset, we employ a hybrid resampling technique, SMOTE-ENN, which integrates the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) to generate synthetic data points while removing noisy, irrelevant instances. This combined technique not only balances the dataset but also helps in mitigating the potential adverse effects of imbalanced data. We evaluate the performance of the logistic regression model on the resampled dataset using common evaluation metrics such as accuracy, F1 score, recall, precision, and the AUC-ROC curve. Additionally, we emphasize the importance of the Area Under the Precision-Recall Curve (AUPRC) as a critical metric for evaluating model performance in imbalanced scenarios. The experimental results demonstrate that logistic regression achieves an impressive 98% accuracy, outperforming other methods and validating the efficacy of our proposed approach for detecting healthcare fraud in imbalanced datasets