ANALYSIS AND PREDICTION OF HEALTH INSURANCE PREMIUM VALUE USING MACHINE LEARNING ALGORITHM - Danica Recca Danendra; Januponsa Dio Firizqi (RADJA PUBLIKA, 2025)
Rising healthcare costs and administrative complexity in the health insurance sector underscore the need for an efficient predictive model to anticipate insurance premium prices. The study explores Machine Learning (ML) techniques to predict the value of health insurance premiums. Also, it aims to provide further insights to stakeholders to create strategies in premium pricing and risk management. This study uses the Kaggle.com datasets and a boosting regression algorithm to compare the accuracy and metric evaluation results in predicting the value of insurance premiums. Feature engineering techniques are applied to improve model performance, reduce over-fitting, and interpret the model to ensure the inclusion of relevant predictors by studying the strengths and limitations of each technique. They overcome this through feature selection, model interpret-ability, scalability, and generalization. Through this comprehensive review, the results of this study aim to provide valuable insights for practitioners, researchers, and policymakers, as well as facilitate informed decision-making in the context of determining the value of health insurance premiums through the use of ML methodologies.