Estimating Medical Insurance Cost using Linear Regression with HyperParameterization, Decision Tree and Random Forest Models

The total health expenditure refers to the total public and private funds spent on health services and amenities, medical and surgical bills and all other healthcare facilities provided. The financing for health is of great significance and plays a crucial role in health systems. To enhance the productivity of human capital, the efficiency and delivery of healthcare services must be uplifted. Reports have shown that from year 2000 to 2018 there had been a gradual escalation of global health expenses and is standing on around 10% of the total GDP of the world. Out of pocket expenses are also high in least developed nations that have lower per capita income. Even though the World Health Organization (WHO) sanctions loans to these countries, these nations are bound to use the money on industrialization only and not their healthcare, education and public welfare sectors. With inflation, the expenses of first-rate healthcare are also rising which makes it fundamental to have health and life insurance plans. Health insurance schemes insured around 514 million people in India in the year 2021, most of which were covered under government schemes only. Since the advent of COVID-19 people have realized the need for having a insurance plan. Most of the companies that are based on the health insurance sector use predictive modelling to improve their services and business process. Machine Learning (ML) algorithms are used to train a model and provide insurance costs estimations. Past data is searched for any pattern or trend in the behaviour history of consumers and then future estimations are evaluated. The proposed project is comprised of different regression models like Linear regression with hyperparameterization , regressors like Decision Tree and Random forest to estimate the approximate insurance expenditure.