A Multifactor Authentication Framework for the National Health Insurance Scheme in Ghana using Machine Learning

Abstract

The creation of the National Health Insurance Scheme (NHIS) in 2005 to replace the traditional “cash and carry” healthcare financial model, was anticipated to offer a safe, reliable, affordable and national coverage healthcare system for the Ghanaian populace. The scheme has recorded several challenges; as a result, policymakers and donor agencies are reconsidering the current NHIS model and are thinking of crafting a better alternate and sustainable financial model for the NHIS. This study seeks to propose a multifactor authentication framework for the national health insurance scheme in Ghana using soft-computing machine learning techniques to minimize the current challenges. It was observed that the proposed system used 1.02 sec to vet 25 claim forms, while the human professional used 120 sec for a single document. The accuracy (91.50%) and F1 (88.52) score measure obtained shows a higher rate of the vetting process by the proposed system.