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@article{195755,
        author = {D.Kanaka Satya and M.Lakshmi Prasanna and P.Manikyamba and P.S.S.Radha Saranya and M.C.S.M.Kondala Rao},
        title = {MEDICAL INSURANCE COST ANALYSIS IDENTIFYING KEY PRICE DRIVERS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3461-3465},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195755},
        abstract = {This work focuses on analyzing medical insurance costs using machine learning techniques to understand the key factors that influence premium amounts. With the rising expenses in healthcare, estimating insurance charges accurately has become essential for both individuals and service providers.
In this study, a publicly available dataset containing attributes such as age, gender, body mass index (BMI), number of dependents, smoking habits, region, lifestyle factors, medical history and financial & policy details was utilized. The data was pre-processed through steps like handling missing values, converting categorical variables into numerical form, and scaling features to improve model efficiency. Among various models, Random Forest Regression was applied due to its ability to capture complex relationships between variables and provide reliable predictions. The model was evaluated using performance metrics such as R² score, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).The results indicate that smoking status, BMI, and age are the most influential factors affecting insurance costs. The study highlights how machine learning can be effectively used not only for prediction but also for gaining meaningful insights into cost-driving factors.},
        keywords = {Cost Analysis, Machine Learning, Medical Insurance, Random Forest Regression.},
        month = {April},
        }