Development of machine learning-based models to predict congenital heart disease: A matched case-control study.

Journal: International journal of medical informatics
PMID:

Abstract

BACKGROUND: The current congenital heart disease (CHD) prediction tools lack adequate interpretability and convenience, hindering the development of personalized CHD management strategies. We developed a machine learning-based risk stratification model for CHD prediction.

Authors

  • Shutong Zhang
    Department of Radiology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 26 Shengli Avenue, Jiangan, Wuhan, 430014, Hubei, China. zhangshutong1960@sina.com.
  • Chenxi Kang
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
  • Jing Cui
    School of Materials and Chemical Engineering, Zhengzhou University of Light Industry, No. 136, Science Avenue, Zhengzhou, 450001, China.
  • Haodan Xue
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
  • Shanshan Zhao
    Department of Ultrasound, Shaoxing People's Hospital (Zhejiang University Shaoxing Hospital), Shaoxing, China.
  • Yukui Chen
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
  • Haixia Lu
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
  • Lu Ye
    Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
  • Duolao Wang
    Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK. duolao.wang@lstmed.ac.uk.
  • Fangyao Chen
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
  • Yaling Zhao
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China.
  • Leilei Pei
    Department of Epidemiology and Health Statistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shaanxi 710061, China. Electronic address: pll_paper@126.com.
  • Pengfei Qu
    Translational Medicine Center, Northwest Women's and Children's Hospital, Xi'an 710061, China; Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Chaoyang, Beijing 100026, China. Electronic address: xinxi3057@163.com.