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  <doi_batch_id>aspg-21-3822-1791475952</doi_batch_id>
  <timestamp>20261008161232</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>International Journal of Neutrosophic Science</full_title>
    <abbrev_title>IJNS</abbrev_title>
    <issn media_type="print">2692-6148</issn>
    <issn media_type="electronic">2690-6805</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>26</volume>
    </journal_volume>
    <issue>3</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Neutrosophic Statistical Analysis on Healthcare Data for Oral Cancer Detection</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Sakshi Taaresh</given_name>
      <surname>Khanna</surname>
      <affiliations>
       <institution>
        <institution_name>Amity Institute of Information Technology, Amity University, Uttar Pradesh, Noida-201301, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Sunil Kumar</given_name>
      <surname>Khatri</surname>
      <affiliations>
       <institution>
        <institution_name>Amity University, Uttar Pradesh, Noida-201301, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Neeraj Kumar</given_name>
      <surname>Sharma</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, Ram Lal Anand College, Benito Juarez Marg, New Delhi-110021, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Oral cancer is presently a growing health concern at the global level, with intense incidences of lifestyle factors. The increasing mortality rates of the diseased shall be controlled with effective early detection mechanisms. However, the traditional statistical approaches in practice fail to deliver in making a precise diagnosis of this cancer due to the intricate and interdependent prevalence of symptoms. This research work provides a solution approach using the potency of neutrosophic statistics in developing neutrosophic-integrated models of random forests and decision trees. Neutrosophic representation of data considering the indeterminacy, values of truth, and falsity facilitates healthcare experts in handling the conflicting patient data. The proposed random forest decision model with neutrosophic logic identifies the significant features, and the neutrosophic decision tree classifier predicts the stages of cancer. The findings are compared with conventional modelling of random forest and decision trees, and it demonstrates the efficiency and precision of neutrosophic statistical analysis in predicting oral cancer. This proposed neutrosophic decision framework will assist and support the medical practitioners and research experts in gaining more insights and deeper comprehension of the cancer progression and suggesting suitable treatment plans to minimize the morbidity rate.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>314</first_page>
     <last_page>330</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3822</item_number>
    </publisher_item>
    <ai:program name="AccessIndicators">
     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/IJNS.260323</doi>
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