<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v2.0 20040830//EN" "journalpublishing.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="2.0" xml:lang="en" article-type="reviewer-report"><front><journal-meta><journal-id journal-id-type="nlm-ta">JMIRx Med</journal-id><journal-id journal-id-type="publisher-id">xmed</journal-id><journal-id journal-id-type="index">34</journal-id><journal-title>JMIRx Med</journal-title><abbrev-journal-title>JMIRx Med</abbrev-journal-title><issn pub-type="epub">2563-6316</issn><publisher><publisher-name>JMIR Publications</publisher-name><publisher-loc>Toronto, Canada</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">v5i1e60853</article-id><article-id pub-id-type="doi">10.2196/60853</article-id><article-categories><subj-group subj-group-type="heading"><subject>Peer-Review Report</subject></subj-group></article-categories><title-group><article-title>Peer Review of &#x201C;Machine Learning&#x2013;Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals&#x201D;</article-title></title-group><contrib-group><contrib contrib-type="author"><name name-style="western"><surname>Chaturvedi</surname><given-names>Akhil</given-names></name><degrees>MS</degrees><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff id="aff1"><institution>Headspace</institution>, <addr-line>San Francisco</addr-line><addr-line>CA</addr-line>, <country>United States</country></aff><contrib-group><contrib contrib-type="editor"><name name-style="western"><surname>Meinert</surname><given-names>Edward</given-names></name></contrib></contrib-group><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>11</day><month>9</month><year>2024</year></pub-date><volume>5</volume><elocation-id>e60853</elocation-id><history><date date-type="received"><day>23</day><month>05</month><year>2024</year></date><date date-type="accepted"><day>23</day><month>05</month><year>2024</year></date></history><copyright-statement>&#x00A9; Akhil Chaturvedi. Originally published in JMIRx Med (<ext-link ext-link-type="uri" xlink:href="https://med.jmirx.org">https://med.jmirx.org</ext-link>), 11.9.2024. </copyright-statement><copyright-year>2024</copyright-year><license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (<ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIRx Med, is properly cited. The complete bibliographic information, a link to the original publication on <ext-link ext-link-type="uri" xlink:href="https://med.jmirx.org/">https://med.jmirx.org/</ext-link>, as well as this copyright and license information must be included.</p></license><self-uri xlink:type="simple" xlink:href="https://xmed.jmir.org/2024/1/e60853"/><related-article related-article-type="companion" ext-link-type="doi" xlink:href="10.1101/2023.11.22.23298939" xlink:title="Preprint (medRxiv)" xlink:type="simple">https://www.medrxiv.org/content/10.1101/2023.11.22.23298939v1</related-article><related-article related-article-type="companion" ext-link-type="doi" xlink:href="10.2196/56993" xlink:title="Preprint (JMIR Preprints)" xlink:type="simple">http://preprints.jmir.org/preprint/56993</related-article><related-article related-article-type="companion" ext-link-type="doi" xlink:href="10.2196/60174" xlink:title="Authors' Response to Peer-Review Reports" xlink:type="simple">https://med.jmirx.org/2024/1/e60174</related-article><related-article related-article-type="companion" ext-link-type="doi" xlink:href="10.2196/56993" xlink:title="Published Article" xlink:type="simple">https://med.jmirx.org/2024/1/e56993</related-article><kwd-group><kwd>hyperglycemia</kwd><kwd>diabetes</kwd><kwd>machine learning</kwd><kwd>hypertension</kwd><kwd>random forest</kwd></kwd-group></article-meta></front><body><p><italic>This is the peer-review report for &#x201C;Machine Learning&#x2013;Based Hyperglycemia Prediction: Enhancing Risk Assessment in a Cohort of Undiagnosed Individuals.&#x201D;</italic></p><sec id="s2"><title>Round 1 Review</title><sec id="s1-1"><title>General Comments</title><p>Overall strong paper [<xref ref-type="bibr" rid="ref1">1</xref>]! This was an interesting study on the use of machine learning to predict hyperglycemia in a cohort of undiagnosed individuals from Nigeria. I feel like this work is a strong contribution to the field of public health, especially within the context of noncommunicable diseases in developing countries. I also like that it is backed well with quantitative methods. The strengths of this manuscript lie in its detailed methodology and its comprehensive data analysis.</p></sec><sec id="s1-2"><title>Specific Comments</title><sec id="s1-2-1"><title>Major Comments</title><list list-type="bullet"><list-item><p>While the study demonstrates a robust analytical approach, it would benefit from external validation with an independent dataset. This would strengthen the findings and ensure the model&#x2019;s generalizability and applicability in different populations.</p></list-item><list-item><p>The manuscript could be improved by providing more context on the selection of the machine learning algorithms used in the study. An explanation of why certain algorithms were chosen and others potentially excluded would offer clarity.</p></list-item></list></sec><sec id="s1-2-2"><title>Minor Comments</title><list list-type="bullet"><list-item><p>The manuscript occasionally uses technical jargon that might not be easily understandable to readers not familiar with machine learning. Simplifying the language or providing brief explanations will make the paper more accessible.</p></list-item><list-item><p>The study&#x2019;s potential for real-world application would be clearer with a section on future work, detailing how these algorithms could be deployed in clinical settings or used in larger-scale studies (I can see how this might be a tangential research direction, but this would still be great given the potential impact).</p></list-item></list><p>Based on this, my recommendation is:</p><p>A: Accept</p></sec></sec></sec></body><back><fn-group><fn fn-type="conflict"><p>None declared.</p></fn></fn-group><ref-list><title>References</title><ref id="ref1"><label>1</label><nlm-citation citation-type="journal"><person-group person-group-type="author"><name name-style="western"><surname>Oyebola</surname><given-names>K</given-names> </name><name name-style="western"><surname>Ligali</surname><given-names>F</given-names> </name><name name-style="western"><surname>Owoloye</surname><given-names>A</given-names> </name><etal/></person-group><article-title>Machine learning&#x2013;based hyperglycemia prediction: enhancing risk assessment in a cohort of undiagnosed individuals</article-title><source>JMIRx Med</source><year>2024</year><volume>5</volume><fpage>e56993</fpage><pub-id pub-id-type="doi">10.2196/56993</pub-id></nlm-citation></ref></ref-list></back></article>