Abstract

Only 1% of Bangladeshi citizens have access to medical insurance, while in most developed countries, medical insurance coverage is 100%. Medical expenses are increasing worldwide due to inflation, an aging population, and long-term health conditions; for this reason, better health insurance policies should be ensured for the people. The introduction of machine learning algorithms in health insurance improves efficiency by 75% and lower cost by 50%, which plays a vital role in providing better insurance plans to individuals. The paper aims to help insurance companies streamline the process of predicting premium prices and thereby limit medical expenses. This study applies 16 different machine learning models to a new dataset of 300 rows and 24 columns to predict the price. After evaluating the machine learning algorithms using six different evaluation metrics, namely R-square, MAE, MSE, RMSE, RMSNE, and MAPE, it was deduced that a combination of Polynomial, Ridge, and XGBoost algorithms in our stacked model performs the best at predicting results with an accuracy of 89.4%.

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