Journal article Open Access

Predict Health Insurance Cost by using Machine Learning and DNN Regression Models

Mohamed hanafy; Omar M. A. Mahmoud

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      <creatorName>Mohamed hanafy</creatorName>
      <affiliation>department of statistic and insurance, Assuit  university, Assuit , Egypt</affiliation>
      <creatorName>Omar M. A. Mahmoud</creatorName>
      <affiliation>department of statistic and insurance, Assuit  university, Assuit , Egypt.</affiliation>
    <title>Predict Health Insurance Cost by using Machine  Learning and DNN Regression Models</title>
    <subject>Regression, machine learning, deep neural network, forecast, insurance</subject>
    <subject subjectScheme="issn">2278-3075</subject>
    <subject subjectScheme="handle">100.1/ijitee.C83640110321</subject>
    <contributor contributorType="Sponsor">
      <contributorName>Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</contributorName>
    <date dateType="Issued">2021-01-30</date>
  <resourceType resourceTypeGeneral="JournalArticle"/>
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    <relatedIdentifier relatedIdentifierType="ISSN" relationType="IsCitedBy" resourceTypeGeneral="JournalArticle">2278-3075</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.35940/ijitee.C8364.0110321</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&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;</description>
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