Identifying Fraudulent Behaviors in Healthcare Claims Using Random Forest Classifier With SMOTEchnique

Abstract

Detecting fraudulent and abusive cases in healthcare is one of the most challenging problems for data mining studies. Existing studies have a lack of real data for analysis and focus on a very partial version of the problem by covering only a specific actor, healthcare service, or disease. In this article, the proposed strategy identifies fraudulent behaviors in Medicare claims data using several predictors as model inputs. The methodology involves preprocessing and model development phases. At the initial phase, the feature mining is done by estimating their feature importance score. The labeling of instances by using the classification rules to the whole dataset. Thus, a transformed dataset is obtained by the model. In the development phase, the RF with SMOTE is applied against the training and testing data. Specifically, SMOTE adapted to balance data and sorts misclassified instances and finds the interesting instances. The results of the proposed model improvises the classifier performance RF with SMOTE when contrast with RF method.