Predict Health Insurance Cost by using Machine Learning and DNN Regression Models
Mohamed hanafy1, Omar M. A. Mahmoud2
1Mohamed hanafy*, Department of Statistic and Insurance, Assuit University, Assuit, Egypt.
2Omar M. A. Mahmoud, Department of Statistic and Insurance, Assuit University, Assuit, Egypt.
Manuscript received on December 18, 2020. | Revised Manuscript received on January 05, 2020. | Manuscript published on January 10, 2021. | PP: 137-143 | Volume-10 Issue-3, January 2021 | Retrieval Number: 100.1/ijitee.C83640110321| DOI: 10.35940/ijitee.C8364.0110321
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Regression, Machine learning, Deep Neural Network, Forecast, Insurance.
Scope of the Article: Machine Learning