<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2d1 20170631//EN" "JATS-journalpublishing1.dtd">
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>ijesm</PublisherName>
      <JournalTitle>International Journal of Engineering, Science and</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>volume 15,issue 3</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>March 2026</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2026</Year>
        <Month>03</Month>
        <Day>11</Day>
      </PubDate>
      <ArticleType>Engineering, Science and Mathematics</ArticleType>
      <ArticleTitle>Machine Learning Approaches for Predicting Diabetes, Stroke, and Cardiovascular Disease: A Systematic Literature Review</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>6</FirstPage>
      <LastPage>19</LastPage>
      <AuthorList>
        <Author>
          <FirstName>Ashish Joshi</FirstName>
          <LastName/>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
          <FirstName>Prof Jeetendra Pande</FirstName>
          <LastName/>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>Y</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>Globally, the primary causes of death and long-term disability are chronic non-communicable diseases (NCDs), especially diabetes mellitus, stroke, and cardiovascular disease (CVD). These diseases are closely interconnected through shared biological mechanisms, including endothelial injury, inflammation, vascular impairment, and metabolic dysfunction. In order to lower mortality and the cost of healthcare, early risk prediction is essential. Machine learning (ML) a sub field of artificial intelligence (AI), has emerged as a powerful tool for predictive healthcare analytics. Many disease-specific machine learning models have been created, however there are still few comparable frameworks that incorporate diabetes</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>Artificial Intelligence, Machine Learning, Diabetes, Stroke, Cardiovascular Disease, Ensemble Learning, Random Forest, XGBoost.</Keywords>
      <URLs>
        <Abstract>https://www.ijesm.co.in/ubijournal-v1copy/journals/abstract.php?article_id=16175&amp;title=Machine Learning Approaches for Predicting Diabetes, Stroke, and Cardiovascular Disease: A Systematic Literature Review</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
  </Article>
</ArticleSet>