Machine Learning Recommendation System For Health Insurance Decision Making In Nigeria
This work addresses the challenge of selecting health insurance plans for individuals in Nigeria, but it is incremental as it applies existing recommendation methods to a new domain-specific dataset.
The authors tackled the problem of low health insurance uptake in Nigeria by developing a content-based recommender system using cosine similarity to filter and recommend the top three health management organizations (HMOs) based on user inputs like location and price, aiming to reduce decision-making barriers.
The uptake of health insurance has been poor in Nigeria, a significant step to improving this includes improved awareness, access to information and tools to support decision making. Artificial intelligence (AI) based recommender systems have gained popularity in helping individuals find movies, books, music, and different types of products on the internet including diverse applications in healthcare. The content-based methodology (item-based approach) was employed in the recommender system. We applied both the K-Nearest Neighbor (KNN) and Cosine similarity algorithm. We chose the Cosine similarity as our chosen algorithm after several evaluations based of their outcomes in comparison with domain knowledge. The recommender system takes into consideration the choices entered by the user, filters the health management organization (HMO) data by location and chosen prices. It then recommends the top 3 HMOs with closest similarity in services offered. A recommendation tool to help people find and select the best health insurance plan for them is useful in reducing the barrier of accessing health insurance. Users are empowered to easily find appropriate information on available plans, reduce cognitive overload in dealing with over 100 options available in the market and easily see what matches their financial capacity.