{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T18:49:01Z","timestamp":1791830941723,"version":"4.3.4"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Diabetes Invest"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Aims\/Introduction<\/jats:title><jats:p>Machine learning algorithms based on the artificial neural network (ANN), support vector machine, naive Bayesian or logistic regression model are commonly used to identify diabetes. This study investigated which approach performed the best and whether muscle strength provided any incremental benefit in identifying undiagnosed diabetes in Chinese adults.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This cross\u2010sectional study enrolled 4,482 eligible participants from eight provinces in China, who were randomly divided into the training dataset (<jats:italic>n<\/jats:italic>\u2009=\u20093,586) and the testing dataset (<jats:italic>n<\/jats:italic>\u2009=\u2009896). Muscle strength was assessed by handgrip strength and the number of chair stands in the 30\u2010s chair stand test. An oral glucose tolerance test was used to ascertain undiagnosed diabetes. The areas under the curve (AUCs) were calculated accordingly and compared with each other.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of the included participants, 233 had newly diagnosed diabetes. All the four machine learning algorithms, which were developed based on nonlaboratory parameters, showed acceptable discriminative ability in identifying undiagnosed diabetes (all AUCs &gt;0.70), with the ANN approach performing the best (AUC 0.806). Adding handgrip strength or the 30\u2010s chair stand test to this approach did not increase the AUC further (<jats:italic>P<\/jats:italic>\u2009=\u20090.39 and 0.26, respectively). Furthermore, compared with the New Chinese Diabetes Risk Score, the ANN approach showed a larger AUC in identifying undiagnosed diabetes (<jats:italic>P<\/jats:italic><jats:sub>comparison<\/jats:sub>\u2009&lt;\u20090.01), regardless of the addition of handgrip strength or the 30\u2010s chair stand test.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The ANN approach performed the best in identifying undiagnosed diabetes in Chinese adults; however, the addition of muscle strength might not improve its efficacy.<\/jats:p><\/jats:sec>","DOI":"10.1111\/jdi.14166","type":"journal-article","created":{"date-parts":[[2024,3,5]],"date-time":"2024-03-05T05:39:04Z","timestamp":1709617144000},"page":"743-750","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Performance of different machine learning algorithms in identifying undiagnosed diabetes based on nonlaboratory parameters and the influence of muscle strength: A cross\u2010sectional study"],"prefix":"10.1111","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4182-6817","authenticated-orcid":false,"given":"Ying","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Endocrine Metabolism, The First People's Hospital of Yunnan Province The Affiliated Hospital of Kunming University of Science and Technology  Kunming China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanhu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of General Practice, School of Medicine, Institute of Diabetes, Zhongda Hospital Southeast University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinli","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Yunnan University  Kunming China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics Yunnan University  Kunming China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donglei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital Southeast University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital Southeast University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zilin","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Endocrinology, School of Medicine, Institute of Diabetes, Zhongda Hospital Southeast University  Nanjing 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