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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Artificial Intelligence, Applications and Innovations</JournalTitle>
      <Issn>3060-7124</Issn>
      <Volume>1</Volume>
      <Issue>Journal of Artificial Intelligence, Application and Inovations </Issue>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>01</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Intelligent Prediction of Cardiovascular Disease Mortality Using Machine Learning Techniques</ArticleTitle>
    <VernacularTitle>Intelligent Prediction of Cardiovascular Disease Mortality Using Machine Learning Techniques</VernacularTitle>
    <FirstPage>78</FirstPage>
    <LastPage>86</LastPage>
    <ELocationID EIdType="doi">10.61838/jaiai.1.1.6</ELocationID>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2023</Year>
        <Month>10</Month>
        <Day>09</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p class="Abstract" style="line-height: 150%;"&gt;&lt;span lang="EN-GB" style="font-size: 11.0pt; line-height: 150%;"&gt;This study focuses on predicting cardiovascular disease (CVD) mortality using various machine learning (ML) techniques. A diverse set of parameters from different categories within the Sleep Heart Health Study (SHHS) dataset is leveraged, and ML techniques including LR, KNN, SVM, RF, ETC, and SGD, are employed. To ensure the reliability of these techniques, 10-fold cross-validation is applied.&lt;/span&gt; &lt;span lang="EN-GB" style="font-size: 11.0pt; line-height: 150%;"&gt;Furthermore, the mutual information technique with K-fold stratified cross-validation is used to determine feature importance, enhancing the model’s interpretability. The proposed approach predicts CVD mortality over a 10 to 15-year period and aims to identify influential parameters to facilitate timely interventions and lifestyle improvements for patients, ultimately contributing to an increased lifespan. Among the algorithms, KNN outperforms others, achieving an accuracy of 77%, an F1-score of 77%, an AUC of 79%, a sensitivity of 77.34%, and a specificity of 76.56%.&lt;/span&gt;&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">CVD mortality</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">ML</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">SHHS</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Cross-Validation</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journalaiai.com/index.php/aiai/article/download/6/6</ArchiveCopySource>
  </Article>
</ArticleSet>
