Research Work on Predictive Premium Model of Medical Cost

Insurance is a protection policy which minimizes or eliminates the impacts of expenses loss caused by different risks. A number of factors determine risk costs. These factors dictate how insurance packages are designed. The effectiveness of certain clauses within an insurance policy can be improved through the use of machine learning (ML). In this study, we utilize individual local health data for forecasting insurance amounts tailored to specific person groups. For the purpose of evaluating the performance of these algorithms, nine regression models were used: Linear Regression, XG Boost Regression, Lasso Regression, Random Forest Regression, Ridge Regression, Decision Tree Regression, KNN Model, Support Vector Regression, and Gradient Boosting Regression. The model was trained on the provided dataset, which included a portion of the data as training data. After training the model, it was tested against real data. The validation of the model was done by comparing the predicted data which was assumed abundant. After that the comparison was carried out between the accuracy of these models. We aim to provide some valuable insights for researchers, practitioners, and policymakers for effective decision-making in healthcare contexts by exploiting machine learning methodologies.