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However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM\/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive\/negative predictive value, F-measure for the positive\/negative preDM\/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM\/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM\/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive\/negative predictive values of 35.2%\/74.5%, positive\/negative F-measures of 38.8%\/70.9%, and Kappa of 0.1 (95%CI: 0.06\u20130.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM\/DM youth (\n                    <jats:italic>p<\/jats:italic>\n                    \u2009=\u20095.23\u2009\u00d7\u200910\n                    <jats:sup>\u22125<\/jats:sup>\n                    ).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM\/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.\n                  <\/jats:p>","DOI":"10.1038\/s41598-021-90406-0","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T06:16:36Z","timestamp":1622096196000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Predicting youth diabetes risk using NHANES data and machine learning"],"prefix":"10.1038","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4848-4633","authenticated-orcid":false,"given":"Nita","family":"Vangeepuram","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po-hsiang","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaurav","family":"Pandey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"issue":"1","key":"90406_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2174\/1573399812666151016101622","volume":"13","author":"M Lotfy","year":"2017","unstructured":"Lotfy, M., Adeghate, J., Kalasz, H., Singh, J. & Adeghate, E. 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