Florida Atlantic University Researchers Describe Findings in Big Data (Data reduction techniques for highly imbalanced medicare Big Data): Information Technology – Big Data

2024 JAN 18 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Current study results on big data have been published. According to news reporting out of Florida Atlantic University by NewsRx editors, research stated, “In the domain of Medicare insurance fraud detection, handling imbalanced Big Data and high dimensionality remains a significant challenge.”

Our news journalists obtained a quote from the research from Florida Atlantic University: “This study assesses the combined efficacy of two data reduction techniques: Random Undersampling (RUS), and a novel ensemble supervised feature selection method. The techniques are applied to optimize Machine Learning models for fraud identification in the classification of highly imbalanced Big Medicare Data. Utilizing two datasets from The Centers for Medicare & Medicaid Services (CMS) labeled by the List of Excluded Individuals/Entities (LEIE), our principal contribution lies in empirically demonstrating that data reduction techniques applied to these datasets significantly improves classification performance. The study employs a systematic experimental design to investigate various scenarios, ranging from using each technique in isolation to employing them in combination. The results indicate that a synergistic application of both techniques outperforms models that utilize all available features and data. Moreover, reduction in the number of features leads to more explainable models.”

According to the news reporters, the research concluded: “Given the enormous financial implications of Medicare fraud, our findings not only offer computational advantages but also significantly enhance the effectiveness of fraud detection systems, thereby having the potential to improve healthcare services.”

For more information on this research see: Data reduction techniques for highly imbalanced medicare Big Data. Journal of Big Data, 2024,11(1):1-41. (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/s40537-023-00869-3.

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

(Our reports deliver fact-based news of research and discoveries from around the world.)