<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>37(1)</volume><submitter>Saberi-Karimian M</submitter><pubmed_abstract>&lt;h4>Background&lt;/h4>The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches.&lt;h4>Methods&lt;/h4>A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate </pubmed_abstract><journal>Journal of clinical laboratory analysis</journal><pagination>e24798</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC9833979</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements.</pubmed_title><pmcid>PMC9833979</pmcid><pubmed_authors>Poudineh M</pubmed_authors><pubmed_authors>Khorasani NY</pubmed_authors><pubmed_authors>Kiyoumarsioskouei A</pubmed_authors><pubmed_authors>Rad ES</pubmed_authors><pubmed_authors>Zo MM</pubmed_authors><pubmed_authors>Ghazizadeh S</pubmed_authors><pubmed_authors>Saberi-Karimian M</pubmed_authors><pubmed_authors>Bajgiran MM</pubmed_authors><pubmed_authors>Ferns G</pubmed_authors><pubmed_authors>Ghayour-Mobarhan M</pubmed_authors><pubmed_authors>Hosseini ZS</pubmed_authors><pubmed_authors>Esmaily H</pubmed_authors><pubmed_authors>Mansoori A</pubmed_authors></additional><is_claimable>false</is_claimable><name>Data mining approaches for type 2 diabetes mellitus prediction using anthropometric measurements.</name><description>&lt;h4>Background&lt;/h4>The aim of this study was to evaluate the anthropometric measurements most associated with type 2 diabetes mellitus (T2DM) using machine learning approaches.&lt;h4>Methods&lt;/h4>A prospective study was designed for a total population of 9354 (43% men and 57% women) aged 35-65. Anthropometric measurements include weight, height, demispan, Hip Circumference (HC), Mid-arm Circumference (MAC), Waist Circumference (WC), Body Roundness Index (BRI), Body Adiposity Index (BAI), A Body Shape Index (ABSI), Body Mass Index (BMI), Waist-to-height Ratio (WHtR), and Waist-to-hip Ratio (WHR) were completed for all participants. The association was assessed using logistic regression (LR) and decision tree (DT) analysis. Receiver operating characteristic (ROC) curve was performed to evaluate </description><dates><release>2023-01-01T00:00:00Z</release><publication>2023 Jan</publication><modification>2025-04-04T20:32:58.028Z</modification><creation>2025-04-04T20:32:58.028Z</creation></dates><accession>S-EPMC9833979</accession><cross_references><pubmed>36510349</pubmed><doi>10.1002/jcla.24798</doi></cross_references></HashMap>