Predicting Health Insurance Claim Amount through Machine Learning Algorithms

Abstract

Using medical cost data, this paper seeks to predict the cost of health insurance claim amount. The aim is to create an appropriate machine learning model for predicting insurance claim amount and calculate individuals' out-of-pocket expenses. This study proposes Machine Learning approaches to predict health insurance claim amount through Random Forest Regression, Multiple Linear Regression, XG Boost Regression, Gradient Boosting Regression, and Decision Tree Regression and the experimental results have demonstrated efficacy in making accurate predictions. Random Forest has a remarkable with 96.7% accuracy compared with other models.

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