The following publications are possibly variants of this publication:
- The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider LabelsRichard A. Bauder, Taghi M. Khoshgoftaar. flairs 2018: 404-409 [doi]
- Identifying Medicare Provider Fraud with Unsupervised Machine LearningRichard A. Bauder, Raquel da Rosa, Taghi M. Khoshgoftaar. iri 2018: 285-292 [doi]
- Medicare Fraud Detection Using Machine Learning MethodsRichard A. Bauder, Taghi M. Khoshgoftaar. icmla 2017: 858-865 [doi]
- A Survey of Medicare Data Processing and Integration for Fraud DetectionRichard A. Bauder, Taghi M. Khoshgoftaar. iri 2018: 9-14 [doi]
- Explainable machine learning models for Medicare fraud detectionJohn T. Hancock, Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar. jbd, 10(1):154, December 2023. [doi]
- Medicare Fraud Detection Using Random Forest with Class Imbalanced Big DataRichard A. Bauder, Taghi M. Khoshgoftaar. iri 2018: 80-87 [doi]
- Big Data fraud detection using multiple medicare data sourcesMatthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder. jbd, 5:29, 2018. [doi]
- Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud DetectionRichard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin. ictai 2018: 137-142 [doi]
- Evaluating Model Predictive Performance: A Medicare Fraud Detection Case StudyRichard A. Bauder, Matthew Herland, Taghi M. Khoshgoftaar. iri 2019: 9-14 [doi]
- Medicare Fraud Detection using CatBoostJohn T. Hancock, Taghi M. Khoshgoftaar. iri 2020: 97-103 [doi]
- Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance ClaimsMatthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar. iri 2017: 579-588 [doi]
- A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper)Richard A. Bauder, Taghi M. Khoshgoftaar. iri 2016: 11-19 [doi]