A novel fraud detection and prevention method for healthcare claim processing using machine learning and blockchain technology

Abstract

Healthcare fraud is a global problem affecting both developing and developed countries. It is the deliberate attempt of the perpetrators to take undue advantage of the inefficiencies in current healthcare systems. Fraud tends to deny legitimate beneficiaries of universal health coverage, especially those under health insurance protection. In this work, we propose using machine learning techniques and blockchain technology to detect and prevent fraud in healthcare, especially in claims processing. A decision tree classification algorithm is adopted to classify the original claims dataset. The extracted knowledge is programmed in the Ethereum blockchain smart contract to detect and prevent healthcare fraud. The comparative experimental results show that the best performing tool achieves a classification accuracy of 97.96% and a sensitivity of 98.09%. This means that the proposed system enhances the blockchain smart contract’s ability to detect fraud with an accuracy of 97.96%.