<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>KMAN Publication Inc. (KMANPUB)</PublisherName>
      <JournalTitle>Health Nexus</JournalTitle>
      <Issn></Issn>
      <Volume>2</Volume>
      <Issue>Serial Number 6</Issue>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>04</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Prediction of Diabetes using Supervised Learning Approach</ArticleTitle>
    <VernacularTitle>Prediction of Diabetes using Supervised Learning Approach</VernacularTitle>
    <FirstPage>103</FirstPage>
    <LastPage>111</LastPage>
    <ELocationID EIdType="doi">10.61838/kman.hn.2.2.12</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>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <Abstract>&lt;p&gt;This paper provides an in-depth evaluation of various supervised machine learning models used for predicting diabetes. It discusses the strengths and limitations of several algorithms, including Decision Trees, Random Forest, Rotation Forest, Ensemble Classifier, K-Star, Simple Bayes, Logistic Regression, Functional Tree, and Perceptron Neural Network. The study utilizes a publicly available diabetes dataset from chistio.ir, which includes 520 samples, comprising 200 diabetic patients and 320 non-diabetic patients, and assesses 16 features. Results are validated on the Weka 3.6 open-source platform, using metrics such as AUC, classification accuracy (CA), F1 score, precision, and recall.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">diabetes prediction</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">diagnosis</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">data mining</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">algorithms</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journals.kmanpub.com/index.php/Health-Nexus/article/download/2457/3459</ArchiveCopySource>
  </Article>
</ArticleSet>
