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/0000-0003-2304-6282 View 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

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/0000-0002-6812-5665 View further author information

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Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

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Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

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