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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/9829
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dc.contributor.authorChamola, Vinay-
dc.date.accessioned2023-03-17T10:37:50Z-
dc.date.available2023-03-17T10:37:50Z-
dc.date.issued2022-
dc.identifier.urihttps://ieeexplore.ieee.org/abstract/document/9945906-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9829-
dc.description.abstractHealth insurance is crucial for each person, bearing in mind the increasing medical costs. COVID-19 has been an eye-opener as to how important it is to have health insurance. Medical emergencies can have a severe emotional and financial impact. Thus, a health insurance policy can help mitigate financial risks in unpredictable circumstances. However, the current insurance system is very expensive, as thousands of people pay the premiums, and very few take the claims. Furthermore, the claim settlement process is excruciatingly long and tiresome. In this article, we focus on establishing a rapid and cost-effective framework for the health insurance market, based on machine learning and blockchain technology. By developing a smart contract, blockchain may eliminate any third-party organizations and make the complete process safer, easier, and more efficient. The contract pays the claim based on the claimant’s documentation. We optimized the premiums using a regression model based on the net amount claimed during the current policy tenure and various other criteria. For anticipating risk, a random forest classifier is used, which aids in the risk-rated premium rebate computation for policyholders for their next term of insurance.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.subjectEEEen_US
dc.subjectBlockchainen_US
dc.subjectEthereumen_US
dc.subjectinsuranceen_US
dc.subjectMachine Learningen_US
dc.subjectRandom forest (RF)en_US
dc.titleA Blockchain and ML-Based Framework for Fast and Cost-Effective Health Insurance Industry Operationsen_US
dc.typeArticleen_US
Appears in Collections:Department of Electrical and Electronics Engineering

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