Empowering Healthcare Accessibility: Machine Learning-Driven Predictive Modeling for Health Insurance Premiums in India

In comparison to many other nations, the government of India devotes a com-paratively small portion of its yearly GDP just 2.5% to public healthcare. People now need health insurance more than ever because of the rising expenses of high-quality healthcare, longer life expectancies, and a shift toward non- communicable diseases. Over the past ten years, the amount of insurance data has increased dramatically, giving insurers access to a wealth of information. Using computer algorithms and machine learning (ML), the health insurance industry uses predictive models to evaluate historical insurance data, predict future benefits based on user behavior, insurance policies, and business preference information, and help create new plans. Machine learning has proven to have great potential in many insurance areas. This research suggests employing ML algorithms to create the ML Health Insurance Prediction System, a real-time insurance cost prediction system, to take advantage of these potential and problems. The goal of this method is to reduce healthcare costs by helping insurance companies determine premium rates quickly and precisely. To predict insurance prices and assess model performance, the suggested model combines multiple regression models, such as Ridge Regression, Lasso Regression, Simple Linear Regression, Multiple Linear Regression, and Polynomial Regression.