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@article{175898,
        author = {Mrs. S. Tejaswi and D. SAI SIRISHA and D. LAKSHMI LIKHITA and S. GANESH and CH. NIRMALA},
        title = {PREDICTION OF HEALTH INSURANCE PREMIUMS USING ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {4571-4576},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175898},
        abstract = {Predicting health insurance premiums has become increasingly vital in the healthcare industry, where fair and accurate cost estimation ensures affordability and transparency for policyholders. This paper presents a machine learning-based approach to predict individual health insurance premiums based on various personal and health-related attributes such as age, gender, BMI, smoking status, number of dependents, and region. The primary goal of the study is to explore how supervised learning algorithms can be used to forecast premium costs with high accuracy. We experiment with multiple regression models including Linear Regression, Random Forest, and XGBoost to evaluate their performance on the dataset. Evaluation metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² score are used to assess model effectiveness. The proposed system aims to support insurance companies in offering better risk evaluation and fair pricing, while also helping users understand the financial impact of their health-related choices.},
        keywords = {Health Insurance, Machine Learning, Premium Prediction, Regression Models, Risk Assessment, Supervised Learning, Feature Engineering, Healthcare Analytics.},
        month = {April},
        }