{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:36:52Z","timestamp":1773794212818,"version":"3.50.1"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"vor","delay-in-days":146,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 112-2811-M-040-001"],"award-info":[{"award-number":["NSTC 112-2811-M-040-001"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 112-2121-M-040-002"],"award-info":[{"award-number":["NSTC 112-2121-M-040-002"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["NSTC 113-2121-M-040-001"],"award-info":[{"award-number":["NSTC 113-2121-M-040-001"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["113-2811-M-040-001"],"award-info":[{"award-number":["113-2811-M-040-001"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004699","name":"Chung Shan Medical University Hospital","doi-asserted-by":"publisher","award":["CSH-2023-D-001"],"award-info":[{"award-number":["CSH-2023-D-001"]}],"id":[{"id":"10.13039\/501100004699","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Diabetes Research"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Type 2 diabetes (T2D) is influenced by lifestyle, genetics, and environmental conditions. By utilizing machine learning techniques, we can enhance the precision of T2D risk prediction by analyzing the complex interactions among these variables. This study was aimed at identifying and predicting key variables linked to T2D within the Taiwanese population.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    The study included 3623 individuals with T2D and 14,492 without. Data on lifestyle and anthropometric measures were obtained from the Taiwan Biobank. Statistical analyses were performed using Base SAS software and SAS Viya.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Traditional models identified body mass index (BMI) and waist\u2013hip ratio (WHR) as significant risk factors for T2D, with odds ratios (OR) of 1.10 (95% confidence interval (CI) 1.09\u20131.12) and 1.10 (95% CI 1.09\u20131.11), respectively. These variables remained crucial in predictive models, with the WHR being the most influential. In the overall population, BMI\u2019s relative importance was 0.57, differing by gender (0.23 in men and 0.62 in women). While cigarette smoking and certain genetic variants (\n                    <jats:italic>CDKAL1<\/jats:italic>\n                    ,\n                    <jats:italic>SLC30A8<\/jats:italic>\n                    ,\n                    <jats:italic>CDKN2B<\/jats:italic>\n                    ,\n                    <jats:italic>KCNQ1<\/jats:italic>\n                    ,\n                    <jats:italic>HHEX<\/jats:italic>\n                    ,\n                    <jats:italic>and TCF7L2<\/jats:italic>\n                    ) were significant in traditional models, their importance decreased in predictive models.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusions:<\/jats:bold>\n                    Among various factors, the WHR emerged as the most critical attribute for T2D, underscoring the complexity of T2D etiology. Overall, the random forest and ensemble classifiers emerge as the most effective models, especially in mixed and female categories, highlighting their robustness in predictive performance.\n                  <\/jats:p>","DOI":"10.1155\/jdr\/5531934","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T02:24:44Z","timestamp":1748399084000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Type 2 Diabetes in Taiwan: Unmasking Influential Factors Through Advanced Predictive 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