-
Article
Open AccessExplainable machine learning models for Medicare fraud detection
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 f...
-
Article
Open AccessInvestigating the relationship between time and predictive model maintenance
A majority of predictive models should be updated regularly, since the most recent data associated with the model may have a different distribution from that of the original training data. This difference may ...
-
Article
Open AccessInvestigating class rarity in big data
In Machine Learning, if one class has a significantly larger number of instances (majority) than the other (minority), this condition is defined as class imbalance. With regard to datasets, class imbalance can bi...
-
Article
Approaches for identifying U.S. medicare fraud in provider claims data
Quality and affordable healthcare is an important aspect in people’s lives, particularly as they age. The rising elderly population in the United States (U.S.), with increasing number of chronic diseases, impl...
-
Article
Open AccessSeverely imbalanced Big Data challenges: investigating data sampling approaches
Severe class imbalance between majority and minority classes in Big Data can bias the predictive performance of Machine Learning algorithms toward the majority (negative) class. Where the minority (positive) clas...
-
Article
Open AccessThe effects of class rarity on the evaluation of supervised healthcare fraud detection models
The United States healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud diffi...
-
Article
Open AccessA survey on addressing high-class imbalance in big data
In a majority–minority classification problem, class imbalance in the dataset(s) can dramatically skew the performance of classifiers, introducing a prediction bias for the majority class. Assuming the positiv...
-
Article
Open AccessBig Data fraud detection using multiple medicare data sources
In the United States, advances in technology and medical sciences continue to improve the general well-being of the population. With this continued progress, programs such as Medicare are needed to help manage...
-
Article
The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data
Healthcare in the United States is a critical aspect of most people’s lives, particularly for the aging demographic. This rising elderly population continues to demand more cost-effective healthcare programs. ...
-
Article
Multivariate outlier detection in medicare claims payments applying probabilistic programming methods
The rising elderly population continues to demand more cost-effective healthcare programs. In particular, Medicare is a vital program serving the needs of the elderly in the United States. The growing number o...
