Florida Atlantic University Researchers Reveal New Findings on Machine Learning (Explainable machine learning models for Medicare fraud detection): Machine Learning

Health Policy and Law Daily

2023 OCT 30 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Investigators publish new report on artificial intelligence. According to news originating from Florida Atlantic University by NewsRx correspondents, research stated, “As a means of building explainable machine learning models for Big Data, we apply a novel ensemble supervised feature selection technique. The technique is applied to publicly available insurance claims data from the United States public health insurance program, Medicare.”

Our news correspondents obtained a quote from the research from Florida Atlantic University: “We approach Medicare insurance fraud detection as a supervised machine learning task of anomaly detection through the classification of highly imbalanced Big Data. Our objectives for feature selection are to increase efficiency in model training, and to develop more explainable machine learning models for fraud detection. Using two Big Data datasets derived from two different sources of insurance claims data, we demonstrate how our feature selection technique reduces the dimensionality of the datasets by approximately 87.5% without compromising performance. Moreover, the reduction in dimensionality results in machine learning models that are easier to explain, and less prone to overfitting. Therefore, our primary contribution of the exposition of our novel feature selection technique leads to a further contribution to the application domain of automated Medicare insurance fraud detection. We utilize our feature selection technique to provide an explanation of our fraud detection models in terms of the definitions of the selected features.”

According to the news reporters, the research concluded: “The ensemble supervised feature selection technique we present is flexible in that any collection of machine learning algorithms that maintain a list of feature importance values may be used. Therefore, researchers may easily employ variations of the technique we present.”

For more information on this research see: Explainable machine learning models for Medicare fraud detection. Journal of Big Data, 2023,10(1):1-31. (Journal of Big Data - https://journalofbigdata.springeropen.com). The publisher for Journal of Big Data is SpringerOpen.

A free version of this journal article is available at https://doi.org/10.1186/s40537-023-00821-5.

Our news editors report that more information may be obtained by contacting John T. Hancock, College of Engineering and Computer Science, Florida Atlantic University. Additional authors for this research include Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar.