Open AccessPredict Health Insurance Cost by using Machine Learning and DNN Regression Models
Author(s) -
Mohamed Hanafy,
Omar M. Mahmoud
Publication year - 2021
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.c8364.0110321
Subject(s) - gradient boosting , random forest , artificial neural network , support vector machine , regression , boosting (machine learning) , computer science , value (mathematics) , regression analysis , machine learning , actuarial science , mean squared error , econometrics , artificial intelligence , statistics , economics , mathematics
Insurance is a policy that eliminates or decreases loss costs occurred by various risks. Various factors influence the cost of insurance. These considerations contribute to the insurance policy formulation. Machine learning (ML) for the insurance industry sector can make the wording of insurance policies more efficient. This study demonstrates how different models of regression can forecast insurance costs. And we will compare the results of models, for example, Multiple Linear Regression, Generalized Additive Model, Support Vector Machine, Random Forest Regressor, CART, XG Boost, k-Nearest Neighbors, Stochastic Gradient Boosting, and Deep Neural Network. This paper offers the best approach to the Stochastic Gradient Boosting model with an MAE value of 0.17448, RMSE value of 0.38018and R -squared value of 85.8295.