Authors - Maysha Fahmida, Mst Ridika Mesbahul, Reza Amini, M. A. Quaium, Md Kamruzzaman Sarker Abstract - Health insurance companies need to optimize their services and pricing while ensuring financial sustainability. This study aims to model health insurance cost by analyzing a person’s future health risk based on their historical health conditions and associated diseases, using mortality and cause-of-death data provided by the Centers for Disease Control and Prevention (CDC). By analyzing this data, we uncover patterns and trends that inform the risk assessment process. We then evaluate the performance of various machine learning models in predicting health risks and estimating insurance costs. The results highlight the effectiveness of data-driven approaches in enhancing risk prediction accuracy and cost estimation. Our findings provide actionable insights for health insurance companies to improve personalized pricing strategies and better understand the factors influencing health risks. Finally, we discuss potential improvements and future directions for leveraging advanced data analytics and machine learning in health risk modeling.
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