Published September 30, 2025 | Version CC-BY-NC-ND 4.0
Journal article Open

Application of Machine Learning Models for Patients Health Insurance Cost Prediction

Creators

  • 1. Assistant Professor, Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.

Contributors

  • 1. Assistant Professor, Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.
  • 2. Associate Professor, Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.
  • 3. Department of Information Technology, JIS College of Engineering, Kalyani (West Bengal), India.

Description

Abstract: The use of machine learning models to forecast health insurance costs based on personal characteristics is examined in this study. Age, sex, BMI, number of children, smoking status, and region were among the demographic variables included in the dataset. It was investigated how well several machine learning methods, such as Random Forest, Gradient Boosting, and Linear Regression, estimated insurance costs. After preprocessing the dataset by scaling numerical features and encoding categorical variables, k-fold cross-validation was employed to train and evaluate the regression models. The coefficient of determination (R2), mean absolute error (MAE), and root mean squared error (RMSE) were used to evaluate performance. According to experimental results, Gradient Boosting performed better than Random Forest and Linear Regression.

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Additional details

Identifiers

DOI
10.35940/ijsce.D3685.15040925
EISSN
2231-2307

Dates

Accepted
2025-09-15
Manuscript Received on 05 August 2025 | Revised Manuscript Received on 06 September 2025 | Manuscript Accepted on 15 September 2025 | Manuscript published on 30 September 2025.

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