With the enormous amount of data stored in files, databases, and other repositories, it is increasingly important, to develop powerful means to analyze and extract interesting knowledge from data. Fraudulent healthcare claims increase the burden on the society. The healthcare fraud detection requires compilation of potentially huge data, involving complex computation and sorting operations. Once such frauds have been detected and classified, data cleaning is applied to it which helps to remove the noise and inconsistencies in the data thereby enhancing its quality. This technique can be used to detect the sale of potentially dangerous medicine by pharmacists thereby preventing such medical fraud.

Medical Fraud Data Mining Data Cleaning Classification