An Empirical Analysis of The Influencing Factors of America’s Medical Insurance Cost

This paper empirically analyzes the factors influencing the cost of medical insurance in the United States. Based on data from 12,761 American individuals sourced from Kaggle, the study examines the relationships between age, gender, body mass index (BMI), diabetes, smoking status, and medical insurance costs. Multiple linear regression models were employed, and model optimization techniques, such as log-linear transformation, were applied to enhance the explanatory power of the analysis. The results indicate that age, BMI, diabetes, and smoking status have significant positive correlations with medical insurance costs, while gender (female) shows a negative correlation. The final log-linear model achieved a high goodness-of-fit (R²=0.694), demonstrating that these factors effectively explain variations in medical insurance costs. The study provides valuable insights for insurance companies in premium pricing and for individuals in health management.