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The uptake of health insurance has been poor in Nigeria, a significant stepto improving this includes improved awareness, access to information and toolsto support decision making. Artificial intelligence (AI) based recommendersystems have gained popularity in helping individuals find movies, books,music, and different types of products on the internet including diverseapplications 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 ourchosen algorithm after several evaluations based of their outcomes incomparison with domain knowledge. The recommender system takes intoconsideration the choices entered by the user, filters the health managementorganization (HMO) data by location and chosen prices. It then recommends thetop 3 HMOs with closest similarity in services offered. A recommendation toolto help people find and select the best health insurance plan for them isuseful in reducing the barrier of accessing health insurance. Users areempowered to easily find appropriate information on available plans, reducecognitive overload in dealing with over 100 options available in the market andeasily see what matches their financial capacity.

Machine Learning Recommendation System For Health Insurance Decision Making In Nigeria