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ORIGINAL RESEARCH
Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models
Wei Xu1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaView further author information
, Zikai Zhang2 Department of Oncology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaView further author information
, Kerong Hu1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaView further author information
, Ping Fang1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaView further author information
, Ran Li1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of China
https://orcid.org/0000-0003-2304-6282View further author information
Dehong Kong1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaView further author information
, Miao Xuan1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaView further author information
, Yang Yue3 School of Mathematics and Statistics, University of Melbourne, Melbourne, AustraliaView further author information
, Dunmin She4 Clinical Medical College, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China;5 Department of Endocrinology, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, Jiangsu, People’s Republic of ChinaCorrespondence[email protected]
View further author information
& View further author information
Ying Xue1 Department of Endocrinology and Metabolism, Tongji Hospital, School of Medicine, Tongji University, Shanghai, People’s Republic of ChinaCorrespondence[email protected]
https://orcid.org/0000-0002-6812-5665View further author information
Pages 2141-2151
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Received 03 Apr 2023, Accepted 11 Jul 2023, Published online: 17 Jul 2023
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