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.

翻译：尼日利亚的健康保险参保率较低，提升这一现状的关键步骤包括增强认知、改善信息获取渠道及提供决策支持工具。基于人工智能的推荐系统已广泛应用于帮助个人在互联网上寻找电影、书籍、音乐及各类商品，并在医疗领域拥有多样化应用。本研究采用基于内容（项目导向）的方法构建推荐系统，应用了K近邻（KNN）算法与余弦相似度算法。经过多轮评估，将算法结果与领域知识对比后，我们最终选定余弦相似度作为核心算法。该推荐系统通过用户输入的偏好信息，依据地理位置和所选价格范围过滤健康管理组织（HMO）数据，推荐服务相似度最高的三家HMO。这种推荐工具能帮助用户查找和选择最佳健康保险计划，有效降低获取健康保险的障碍。用户得以轻松获取现有保险计划的适用信息，减少面对市场百余种选项时的认知负荷，并直观匹配自身经济能力。