About this document
Supervised Learning in Health Fraud Detection by navin is a document available to read on EtoBox.
This paper discusses the impact of supervised learning methodologies on health insurance fraud detection, highlighting the increasing prevalence of fraud as the number of policyholders rises. It emphasizes the importance of data mining techniques, particularly supervised learning, in identifying fraudulent claims and improving the efficiency of fraud detection processes. The authors conclude that while supervised learning offers higher accuracy, challenges in obtaining labeled data necessitate exploring hyb
- Author
- navin
- Language
- EN