Health Insurance Cost Prediction Using Machine Learning

Abstract

Amidst the backdrop of escalating healthcare costs, a substantial share of the GDP is allocated to health-related expenditures. This study employs machine learning algorithms, including Random Forest Regression, Gradient Boosted Trees, Linear Regression, and Support Vector Machine, to forecast health insurance costs. The primary objective is to empower individuals in making informed decisions about health coverage based on their unique health attributes. Additionally, the research seeks to aid policymakers in identifying providers with higher costs and implementing targeted cost-containment measures. By evaluating algorithm performance on a health insurance dataset, the study underscores the significance of early cost estimation to guide individuals in selecting suitable coverage. In addressing the pressing need for effective management of healthcare expenses, the findings of this research contribute not only to individual decision-making but also provide valuable insights for policymakers striving to strike a balance between quality healthcare provision and fiscal responsibility. The utilization of machine learning in predicting health insurance costs is pivotal for creating a more transparent and efficient healthcare ecosystem. This research endeavors to foster a nuanced understanding of cost dynamics, empowering both individuals and policymakers in navigating the complexities of the contemporary healthcare landscape.