Journal article Open Access

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

Mohamed hanafy; Omar M. A. Mahmoud

MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="">
  <datafield tag="041" ind1=" " ind2=" ">
    <subfield code="a">eng</subfield>
  <datafield tag="653" ind1=" " ind2=" ">
    <subfield code="a">Regression, machine learning, deep neural network, forecast, insurance</subfield>
  <controlfield tag="005">20220111134853.0</controlfield>
  <controlfield tag="001">5837433</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">department of statistic and insurance, Assuit  university, Assuit , Egypt.</subfield>
    <subfield code="a">Omar M. A. Mahmoud</subfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Publisher</subfield>
    <subfield code="4">spn</subfield>
    <subfield code="a">Blue Eyes Intelligence Engineering  and Sciences Publication(BEIESP)</subfield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">680218</subfield>
    <subfield code="z">md5:f2b540366fe94595ee88f5f76f02d39d</subfield>
    <subfield code="u"></subfield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2021-01-30</subfield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o"></subfield>
  <datafield tag="909" ind1="C" ind2="4">
    <subfield code="c">137-143</subfield>
    <subfield code="n">3</subfield>
    <subfield code="p">International Journal of Innovative Technology and Exploring Engineering (IJITEE)</subfield>
    <subfield code="v">10</subfield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">department of statistic and insurance, Assuit  university, Assuit , Egypt</subfield>
    <subfield code="a">Mohamed hanafy</subfield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Predict Health Insurance Cost by using Machine  Learning and DNN Regression Models</subfield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u"></subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2"></subfield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">ISSN</subfield>
    <subfield code="0">(issn)2278-3075</subfield>
  <datafield tag="650" ind1="1" ind2=" ">
    <subfield code="a">Retrieval Number</subfield>
    <subfield code="0">(handle)100.1/ijitee.C83640110321</subfield>
  <datafield tag="520" ind1=" " ind2=" ">
    <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>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">issn</subfield>
    <subfield code="i">isCitedBy</subfield>
    <subfield code="a">2278-3075</subfield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.35940/ijitee.C8364.0110321</subfield>
    <subfield code="2">doi</subfield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">article</subfield>
Views 50
Downloads 21
Data volume 14.3 MB
Unique views 47
Unique downloads 21


Cite as