Deep Learning-Based Medical Insurance Cost Prediction Using Personal and Health Attributes

Abstract

The purpose of an insurance policy is to lessen or eliminate the monetary impact of certain risks and unanticipated events. The significance of accurate cost prediction has grown due to the ongoing increase in healthcare prices and the fact that health insurance premiums are affected by several demographic and health-related factors. Insurance companies, lawmakers, and people may all benefit from accurate cost projections for healthcare when making coverage and budgeting decisions. To estimate insurance costs and analyses complicated correlations among health characteristics, machine learning (ML) and deep learning (DL) methods provide useful ways. The purpose of this research was to create ML and DL models that could predict health insurance premiums based on important demographic and health-related characteristics. With R²=92.94%, RMSE=0.27, MSE=0.0727, and MAE=0.143, the suggested Recurrent Neural Network (RNN) performed admirably. These outcomes prove that the suggested RNN can successfully identify patterns and generate reliable estimates of insurance premiums. Healthcare cost management and data-driven insurance planning can both benefit from the created method.