# ML-Driven Approaches to Combat Medicare Fraud: Advances in Class Imbalance Solutions, Feature Engineering, Adaptive Learning, and Business Impact

**Authors:** Dorsa Farahmandazad, Kasra Danesh  
**arXiv:** [2502.15898](https://arxiv.org/abs/2502.15898)  
**Date:** 2025-02-21  
**Summary page:** https://www.summarizepaper.com/en/paper/2502.15898v1/

## Key points

- Medicare fraud is a pervasive issue in healthcare systems, leading to financial losses and compromising care quality for legitimate beneficiaries.
- A study by Dorsa Farahmandazad and Kasra Danesh explores the use of machine learning (ML) techniques to enhance Medicare fraud detection.
- The research addresses obstacles such as class imbalance, high-dimensional data management, and evolving fraud patterns.
- Five ML models were trained and evaluated: Random Forest, KNN, LDA, Decision Tree, and AdaBoost.
- Optimization techniques include resampling SMOTE method for class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic/procedural codes.
- The top-performing algorithm achieved a training accuracy of 99.2%, validation accuracy of 98.8%, and F1-score of 98.4%.
- Other models showed varying levels of performance with validation accuracies ranging from 63.3% to 96.3%.
- Advanced resampling techniques and adaptive learning approaches are crucial for effective Medicare fraud detection.
- Future research should explore explainable AI methodologies and hybrid model combinations to enhance interpretability and performance metrics in fraud detection systems.

## Summary

Medicare fraud is a pervasive issue within healthcare systems that leads to substantial financial losses and compromises the quality of care for legitimate beneficiaries. In response to this challenge, a study conducted by Dorsa Farahmandazad and Kasra Danesh explores the application of machine learning (ML) techniques to enhance Medicare fraud detection. The research focuses on addressing key obstacles such as class imbalance, managing high-dimensional data, and adapting to evolving fraud patterns. The study utilizes a comprehensive dataset encompassing inpatient claims, outpatient claims, and beneficiary information to train and evaluate five distinct ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. To optimize model performance, various data preprocessing techniques are employed including the resampling SMOTE method to tackle class imbalance issues, feature selection for dimensionality reduction, and the aggregation of diagnostic and procedural codes. Among the models tested, emerges as the top-performing algorithm with an impressive training accuracy of 99.2% and validation accuracy of 98.8%, achieving an F1-score of 98.4%. The  also demonstrates strong performance with a validation accuracy of 96.3%. However, and  exhibit moderate results with validation accuracies of 79.2% and 81.1% respectively., on the other hand,struggles with a validation accuracy of 63.3% along with a low recall rate of 16.6%. The findings underscore the significance of advanced resampling techniques,and adaptive learning approaches in effectively detecting Medicare fraud patterns.This study highlights the potential impact of machine learning in addressing the complexities associated with fraud detection in healthcare settings. Moving forward, future research endeavors should explore explainable AI methodologies and hybrid model combinations to enhance interpretability while improving overall performance metrics. By developing scalable and reliable fraud detection systems that safeguard healthcare resources and beneficiaries alike, advancements in ML-driven approaches hold promise for combating Medicare fraud more effectively in the future.