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This essay delves into the intricate relationship between various factors and healthcare insurance charges, employing two sophisticated mathematical-statistical models: machine learning such as linear regression and skewness and kurtosis. The dataset utilized in this analysis is sourced from the reputable data science platform, Kaggle. The findings of this study indicate that several key factors play a crucial role in determining health insurance pricing. Specifically, age and smoking status are identified as significant influencers. However, the BMI (Body Mass Index) cannot affect the charges. As age increases, so do healthcare charges, reflecting the higher likelihood of health issues in older individuals. Similarly, smokers are charged more due to the increased health risks associated with smoking. Additionally, higher BMI values are not linked to higher insurance charges. Moreover, the study highlights a direct correlation between these factors and healthcare insurance pricing. As age and smoking status increase, there is a corresponding increase in insurance charges. This underscores the importance of these factors in determining the cost of healthcare insurance.
Tang et al. (Thu,) studied this question.