Use of Ensemble Based Approach to Predict Health Insurance Premium at Early Stage

Health insurance is one of the most critical purchases a person makes every year. One-third of GDP is spent on health insurance, and everyone requires some degree of health care. The healthcare rates constantly fluctuating every year due of different variables such as medical changes, pharmaceutical trends, and political considerations. There is a need to construct a mathematical model to anticipate premiums based on numerous criteria that effect the rates. The premium rates are established by insurance companies using complicated algorithms based on past years' health care consumption and the total number of enrollments. In this research, an ensemble-based regression model to forecast future premiums is suggested. The suggested model is compared with the usual four regression models. It is demonstrated using multiple metrics that the suggested model always produces superior outcomes.