Your browser doesn't support javascript.
loading
Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study.
Hu, Xiang; Li, Xue-Ke; Wen, Shiping; Li, Xingyu; Zeng, Tian-Shu; Zhang, Jiao-Yue; Wang, Weiqing; Bi, Yufang; Zhang, Qiao; Tian, Sheng-Hua; Min, Jie; Wang, Ying; Liu, Geng; Huang, Hantao; Peng, Miaomiao; Zhang, Jun; Wu, Chaodong; Li, Yu-Ming; Sun, Hui; Ning, Guang; Chen, Lu-Lu.
Afiliação
  • Hu X; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Li XK; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Wen S; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Li X; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Zeng TS; Centre for Artificial Intelligence, Faculty of Engineering Information Technology, University of Technology Sydney, Ultimo, NSW, 2007, Australia.
  • Zhang JY; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Wang W; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Bi Y; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Zhang Q; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Tian SH; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Min J; Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao-Tong Un
  • Wang Y; Department of Endocrinology and Metabolism, State Key Laboratory of Medical Genomes, National Clinical Research Center for Metabolic Diseases, Shanghai Clinical Center for Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao-Tong Un
  • Liu G; Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Huang H; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Peng M; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Zhang J; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Wu C; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Li YM; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Sun H; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
  • Ning G; Department of Endocrinology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Chen LL; Hubei Provincial Clinical Research Center for Diabetes and Metabolic Disorders, Wuhan, China.
Heliyon ; 8(12): e12343, 2022 Dec.
Article em En | MEDLINE | ID: mdl-36643319
Palavras-chave
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article