Application of Standard Machine Learning Models for Medicare Fraud Detection with Imbalanced Data

Dorsa Farahmandazad (), Kasra Danesh and Hossein Fazel Najaf Abadi ()
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Dorsa Farahmandazad: College of Business, Florida Atlantic University, Boca Raton, FL 33431, USA
Kasra Danesh: Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
Hossein Fazel Najaf Abadi: Department of Computer Science, Columbia College, Vancouver, BC V6B 1Z3, Canada

Risks, 2025, vol. 13, issue 10, 1-25

Abstract: Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud patterns. A dataset comprising inpatient claims, outpatient claims, and beneficiary details was used to train and evaluate five ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. Data preprocessing techniques included resampling SMOTE method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and procedural codes. Random Forest emerged as the best-performing model, achieving a training accuracy of 99.2% and validation accuracy of 98.8%, and F1-score (98.4%). The Decision Tree also performed well, achieving a validation accuracy of 96.3%. KNN and AdaBoost demonstrated moderate performance, with validation accuracies of 79.2% and 81.1%, respectively, while LDA struggled with a validation accuracy of 63.3% and a low recall of 16.6%. The results highlight the importance of advanced resampling techniques, feature engineering, and adaptive learning in detecting Medicare fraud effectively. This study underscores the potential of machine learning in addressing the complexities of fraud detection. Future work should explore explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection systems that protect healthcare resources and beneficiaries.

Keywords: Medicare fraud detection; machine learning; class imbalance; feature selection; adaptive learning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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