Performance Evaluation of Regression Models in Predicting the Cost of Medical Insurance - Cenita, Jonelle Angelo S.; Asuncion, Paul Richie F.; Victoriano, Jayson M. (International Journal of Computing Sciences Research; Vol 7 (2023): Volume 7, 2023)
Purpose – The study aimed to evaluate the regression models' performance in predicting the cost of medical insurance. The Three (3) Regression Models in Machine Learning namely Linear Regression, Gradient Boosting, and Support Vector Machine were used. The performance will be evaluated using the metrics RMSE (Root Mean Square), r2 (R Square), and K-Fold Cross-validation. The study also sought to pinpoint the feature that would be most important in predicting the cost of medical insurance. Method – The methodology of the study is anchored on the knowledge discovery in databases (KDD) process. (KDD) process refers to the overall process of discovering useful knowledge from data. Results – The performance evaluation results reveal that among the three (3) Regression models, Gradient boosting received the highest r2 (R Square) 0.892 and the lowest RMSE (Root Mean Square) 1336.594.  Furthermore, the 10-Fold Cross-validation weighted mean findings are not significantly different from the r2 (R Square) results of the three (3) regression models. In addition, Exploratory Data Analysis (EDA) using a box plot of descriptive statistics observed that in the charges and smoker features the median of one group lies outside of the box of the other group, so there is a difference between the two groups. Conclusion – In conclusion, Gradient boosting appears to perform better among the three (3) regression models. K-Fold Cross-Validation concluded that the three (3) regression models are good. Moreover, Exploratory Data Analysis (EDA) using a box plot of descriptive statistics ceases that the highest charges are due to the smoker feature. Recommendations – Gradient boosting model be used in predicting the cost of medical insurance. Research Implications – Utilizing an accurate regression model to predict medical costs can aid medical insurance organizations in prioritizing the allocation of limited care management resources as it plays a role in the development of insurance policies.