Abstract: In recent years, the rising cost of healthcare has made medical insurance an essential component of financial planning. However, accurately estimating insurance charges remains a challenging task due to the influence of multiple factors such as age, gender, body mass index (BMI), lifestyle habits, and medical history. Traditional methods used by insurance companies often rely on manual calculations and generalized assumptions, which may lead to inaccurate pricing and lack of transparency.
This paper presents a machine learning-based approach for predicting medical insurance costs using historical data. The proposed system analyzes key features including age, BMI, number of dependents, smoking status, and region to identify patterns that influence insurance charges. Various machine learning algorithms such as Linear Regression, Decision Tree, Random Forest, and Gradient Boosting are implemented and compared to determine the most accurate predictive model.
The dataset is preprocessed through data cleaning, feature encoding, and normalization to improve model performance. The models are trained and evaluated using appropriate performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared score. Among the models tested, ensemble techniques like Random Forest and Gradient Boosting demonstrate superior prediction accuracy due to their ability to handle complex, non-linear relationships in the data.
The results show that machine learning can significantly improve the accuracy and efficiency of insurance cost prediction compared to traditional methods. This system can assist insurance companies in fair pricing strategies and help individuals estimate their medical expenses more effectively.
In conclusion, the proposed model highlights the potential of machine learning in transforming the healthcare insurance sector by providing data-driven, transparent, and reliable cost predictions.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14424