January 30, 2021 Journal article Open Access

Mohamed hanafy; Omar M. A. Mahmoud

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    <subfield code="a">Regression, machine learning, deep neural network, forecast, insurance</subfield>
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    <subfield code="u">department of statistic and insurance, Assuit  university, Assuit , Egypt.</subfield>
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    <subfield code="p">International Journal of Innovative Technology and Exploring Engineering (IJITEE)</subfield>
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    <subfield code="u">department of statistic and insurance, Assuit  university, Assuit , Egypt</subfield>
    <subfield code="a">Mohamed hanafy</subfield>
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    <subfield code="a">Predict Health Insurance Cost by using Machine  Learning and DNN Regression Models</subfield>
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    <subfield code="a">&lt;p&gt;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, XGBoost, 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.&lt;/p&gt;</subfield>
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