Enhancing Medicare Fraud Detection through Machine Learning with SMOTE-ENN
DOI:
https://doi.org/10.15662/IJEETR.2026.0802038Keywords:
Medicare Fraud Detection, Machine Learning, SMOTE-ENN, Imbalanced Data, Classification, Healthcare AnalyticsAbstract
Healthcare fraud detection has become a critical challenge due to the increasing volume of Medicare claims and the presence of highly imbalanced datasets. Traditional rule-based systems are often inefficient and fail to detect complex fraud patterns. This paper proposes a hybrid machine learning framework utilizing the SMOTE-ENN technique to effectively balance the dataset and improve classification performance. Various machine learning algorithms, including Random Forest, Logistic Regression, and Decision Trees, are applied to detect fraudulent claims. The proposed approach significantly improves precision, recall, and F1-score compared to traditional models. Experimental results demonstrate that the hybrid sampling method enhances fraud detection accuracy and reduces false positives, making it suitable for real-world healthcare systems.
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