Blockchain-assisted healthcare insurance fraud detection framework using ensemble learning

In the landscape of advancing healthcare, characterized by prolonged life expectancy and technological strides, healthcare insurance stands as a cornerstone, enabling individuals to access crucial medical services. The increasing prevalence of fraudulent activities in healthcare insurance claims has led to the implementation of stringent and complex claiming procedures. The detection of fraudulent activities carried out by healthcare claim providers is paramount. This paper proposes a novel solution: an ensemble learning-based approach fortified by blockchain security for healthcare insurance claim fraud detection. Leveraging decentralized blockchain technology ensures robust data security, safeguarding confidential healthcare and patient information. The efficacy of our methodology is assessed through a comparative analysis, pitting ensemble learning techniques specifically bagging classification and stacking against conventional individual Machine Learning Algorithms (MLAs). Moreover, our innovative approach goes beyond traditional methods by integrating various forms of patient data, including inpatient data, out-patient data, and beneficiary data. This comprehensive integration enhances the real-world applicability of our solution, providing a more holistic perspective on healthcare insurance claim fraud detection. The evaluation encompasses diverse performance metrics, including accuracy, precision, recall, Receiver Operating Characteristic (ROC), 11-score, and the confusion matrix. Additionally, a comprehensive cost assessment is conducted on the integrated smart contract functionalities. This study introduces a resilient and efficient approach to combat fraudulent activities in healthcare insurance claims.