Risk prediction model of metabolic syndrome in perimenopausal women based on machine learning.

Journal: International journal of medical informatics
Published Date: May 9, 2024

Abstract

INTRODUCTION: Metabolic syndrome (MetS) is considered to be an important parameter of cardio-metabolic health and contributing to the development of atherosclerosis, type 2 diabetes. The incidence of MetS significantly increases in postmenopausal women, therefore, the perimenopausal period is considered a critical phase for prevention. We aimed to use four machine learning methods to predict whether perimenopausal women will develop MetS within 2 years.

Authors

  • Wang Xiaoxue
    Department of Obstetrics and Gynecology, Peking University Ninth School of Clinical Medicine, Beijing Shijitan Hospital, Beijing 100038, China.
  • Wang Zijun
    Department of Obstetrics and Gynecology, Peking University Ninth School of Clinical Medicine, Beijing Shijitan Hospital, Beijing 100038, China.
  • Chen Shichen
    School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
  • Yang Mukun
    Department of Obstetrics and Gynecology, Peking University Ninth School of Clinical Medicine, Beijing Shijitan Hospital, Beijing 100038, China.
  • Chen Yi
    Department of BMC Medical Imaging, Nanchang Hangkong University, 330063, Nanchang, China.
  • Miao Linqing
    Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China.
  • Bai Wenpei
    Department of Obstetrics and Gynecology, Peking University Ninth School of Clinical Medicine, Beijing Shijitan Hospital, Beijing 100038, China. Electronic address: Baiwp@bjsjth.cn.

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