An Intelligent Machine Learning Framework for Health Insurance Claim Cost Prediction Using Healthcare Risk Factors
Authors-M.Anitha, Patibandla Sushmitha
Keyword-Health Insurance, Insurance Claim Prediction, Machine Learning, Healthcare Analytics, Random Forest Regressor, Gradient Boosting Regressor, Support Vector Regression, Linear Regression, Predictive Analytics, Cost Forecasting, Healthcare Risk Assessment, Data Mining.
For insurance companies to maximize premium computation, enhance financial planning, and facilitate efficient risk management, accurate cost projections of health insurance claims are cru-cial. The intricate links between lifestyle factors, health issues, and demographic traits that are frequently difficult for conventional statistical prediction tools to capture might occasionally hinder prediction performance. Using a variety of healthcare-related factors, such as age, gender, body mass index (BMI), smoking habits, number of dependents, genetic illnesses, occupation, and residential area, the intelligent machine learning method presented in this study predicts health insurance claim expenses. To increase forecasting accuracy and decision-making, the suggested approach combines feature engineering, predictive modeling, and meticulous data pretreatment. Standard performance metrics like R-squared (R²) and Mean Absolute Error (MAE) are used to create and assess regression approaches including Linear Regression, Sup-port Vector Regression (SVR), Random Forest Regressor, and Gradient Boosting Regressor. An experimental study found that while Gradient Boosting also produces comparable forecast-ing performance, the Random Forest Regressor offers the highest prediction accuracy by suc-cessfully capturing nonlinear linkages among health-related indicators. Insurance businesses can improve pricing strategies, lower financial uncertainty, anticipate future claim costs more accu-rately, and construct customized insurance policies more easily with the help of the suggested framework. By offering scalable, dependable, and data-driven prediction capabilities appropriate for contemporary health insurance administration systems, the developed method further illus-trates the useful advantages of machine learning for intelligent healthcare analytics.
Doi-[https://doi.org/10.5281/zenodo.21735431]