Application of Machine Learning Models for Patients Health Insurance Cost Prediction
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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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