Richard A. Bauder

Preston Billion Polak

IEEE Access, 2026

2025

A comprehensive survey on machine learning for workplace injury analysis: risk prediction, return to work strategies, and demographic insights.

[BibT_eX]

[DOI]

Gonzalo A. Vivian

J. Big Data, December, 2025

SHAP as a Data Reduction Technique for Highly Imbalanced Big Data.

[BibT_eX]

[DOI]

John T. Hancock

Int. J. Artif. Intell. Tools, 2025

An Interpretable and Efficient Random Undersampling-enhanced SHAP Framework for Medicare Fraud Detection.

[BibT_eX]

[DOI]

Qianxin Liang

Proceedings of the 11th IEEE International Conference on Collaboration and Internet Computing, 2025

2024

Enhancing Medicare Fraud Detection: Random Undersampling Followed by SHAP-Driven Feature Selection with Big Data.

[BibT_eX]

[DOI]

Qianxin Liang

Proceedings of the 36th IEEE International Conference on Tools with Artificial Intelligence, 2024

2023

Explainable machine learning models for Medicare fraud detection.

[BibT_eX]

[DOI]

John T. Hancock

Huanjing Wang

J. Big Data, December, 2023

A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers.

[BibT_eX]

[DOI]

John T. Hancock

Proceedings of the 35th IEEE International Conference on Tools with Artificial Intelligence, 2023

2020

Investigating the relationship between time and predictive model maintenance.

[BibT_eX]

[DOI]

Naeem Seliya

J. Big Data, 2020

Investigating class rarity in big data.

[BibT_eX]

[DOI]

J. Big Data, 2020

A study on rare fraud predictions with big Medicare claims fraud data.

[BibT_eX]

[DOI]

Intell. Data Anal., 2020

2019

The effects of class rarity on the evaluation of supervised healthcare fraud detection models.

[BibT_eX]

[DOI]

J. Big Data, 2019

Severely imbalanced Big Data challenges: investigating data sampling approaches.

[BibT_eX]

[DOI]

J. Big Data, 2019

Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on Information Reuse and Integration for Data Science, 2019

The Effect of Time on the Maintenance of a Predictive Model.

[BibT_eX]

[DOI]

Naeem Seliya

Proceedings of the 18th IEEE International Conference On Machine Learning And Applications, 2019

2018

Machine Learning Algorithms with Big Medicare Fraud Data.

[BibT_eX]

[DOI]

PhD thesis, 2018

A survey on addressing high-class imbalance in big data.

[BibT_eX]

[DOI]

Naeem Seliya

J. Big Data, 2018

Big Data fraud detection using multiple medicare data sources.

[BibT_eX]

[DOI]

J. Big Data, 2018

The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data.

[BibT_eX]

[DOI]

Health Inf. Sci. Syst., 2018

Identifying Medicare Provider Fraud with Unsupervised Machine Learning.

[BibT_eX]

[DOI]

Raquel da Rosa

Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration, 2018

Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration, 2018

A Survey of Medicare Data Processing and Integration for Fraud Detection.

[BibT_eX]

[DOI]

Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration, 2018

Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection.

[BibT_eX]

[DOI]

Proceedings of the IEEE 30th International Conference on Tools with Artificial Intelligence, 2018

An Empirical Study on Class Rarity in Big Data.

[BibT_eX]

[DOI]

Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Fraud Detection with a Limited Number of Known Fraudulent Medicare Providers.

[BibT_eX]

[DOI]

Amri Napolitano

Proceedings of the Thirty-First International Florida Artificial Intelligence Research Society Conference, 2018

The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First International Florida Artificial Intelligence Research Society Conference, 2018

2017

Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Information Reuse and Integration, 2017

Estimating Outlier Score Probabilities.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Information Reuse and Integration, 2017

Medicare Fraud Detection Using Machine Learning Methods.

[BibT_eX]

[DOI]

Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

Multivariate Anomaly Detection in Medicare using Model Residuals and Probabilistic Programming.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference, 2017

2016

A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper).

[BibT_eX]

[DOI]

Proceedings of the 17th IEEE International Conference on Information Reuse and Integration, 2016

Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims.

[BibT_eX]

[DOI]

Aaron N. Richter

Proceedings of the 28th IEEE International Conference on Tools with Artificial Intelligence, 2016

A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims.

[BibT_eX]

[DOI]