Health Insurance Factor Analysis

This paper explores the intricacies of health insurance costs in the United States, emphasizing the Affordable Care Act's (ACA) role in reform. Employing a "health insurance factor analysis" approach, the study identifies key determinants using data visualization and regression models. Factors such as age, BMI, family size, gender, smoking status, and geographic region significantly influence insurance prices. The research evaluates various regression models and neural networks, with linear regression standing out for its high accuracy. The findings underscore the impact of smoking, medical history, and coverage level on insurance costs. Despite dataset limitations and sample size considerations, this study contributes to a nuanced understanding of health insurance pricing, aiding insurers, policymakers, and individuals in decision-making. By integrating data insights and predictive models, the research advances comprehension of the complex relationships shaping health insurance costs. The ultimate goal is to optimize insurance programs and promote accessible and affordable health insurance for all.