Advanced Intelligence Health Insurance Cost Prediction Using Random Forest

Abstract

In the domains of computational and applied mathematics, soft computing, fuzzy logic, and machine learning (ML) are well-known research areas. ML is one of the computational intelligence aspects that may address diverse difficulties in a wide range of applications and systems when it comes to the exploitation of historical data. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. To address this problem, a study was conducted that provides a computational intelligence approach for predicting healthcare insurance costs using a series of machine learning algorithms. The proposed research approach uses various regression models, including Linear Regression, Support Vector Regression, Ridge Regressor, Stochastic Gradient Boosting, XGBoost, Decision Tree, Random Forest Regress or, Multiple Linear Regression, and k-Nearest Neighbors. For this purpose, a medical insurance cost dataset was acquired from the KAGGLE repository, and machine learning methods were used to show how different regression models can forecast insurance costs and to compare the models’ accuracy. The results showed that the Stochastic Gradient Boosting (SGB) model outperformed the others with a cross-validation value of 0.858 and an RMSE value of 0.340, providing86% accuracy.

Keywords: Decision Tree, Random Forest Regression, Multiple Linear Regression