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  <doi_batch_id>aspg-3-1671-1791417685</doi_batch_id>
  <timestamp>20261008000125</timestamp>
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   <depositor_name>American Scientific Publishing Group</depositor_name>
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  <journal>
   <journal_metadata language="en">
    <full_title>Fusion: Practice and Applications</full_title>
    <abbrev_title>FPA</abbrev_title>
    <issn media_type="print">2770-0070</issn>
    <issn media_type="electronic">2692-4048</issn>
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   <journal_issue>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <journal_volume>
     <volume>11</volume>
    </journal_volume>
    <issue>1</issue>
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   <journal_article publication_type="full_text">
    <titles>
     <title>A Review on Artificial Intelligence and Quantum Machine Learning for Heart Disease Diagnosis: Current Techniques, Challenges and Issues, Recent Developments, and Future Directions</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Huda Ghazi</given_name>
      <surname>Enad</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Science Department, College of Computer Science &amp; Information Technology, University of Anbar, Anbar, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mazin Abed</given_name>
      <surname>Mohammed</surname>
      <affiliations>
       <institution>
        <institution_name>Computer Science Department, College of Computer Science &amp; Information Technology, University of Anbar, Anbar, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This study presents a comprehensive analysis of the existing techniques and applications of artificial intelligence (AI) to cardiovascular disease diagnosis. The application of AI to the diagnosis of cardiac diseases can enhance diagnostic precision, diagnostic throughput, and patient outcomes. This literature survey analyzes state-of-the-art AI-based methods, rates their efficiency, examines potential future research and development avenues, and finds challenges and limitations, providing a foundational overview of main developments in AI, machine learning, deep learning, and quantum computing in relation to heart disease prevention. This study seeks to guide the use of AI-based techniques for heart disease detection, having an ultimate objective of enhancing patient outcomes through research and development. This review mainly emphasizes the significance of further studying and advancing AI for its ability to revolutionize the diagnosis and management of heart diseases.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>08</first_page>
     <last_page>25</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1671</item_number>
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     <doi>10.54216/FPA.110101</doi>
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