Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.

Journal: Frontiers in public health
PMID: 36267989

Abstract

OBJECTIVE: The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the risk of hypertension for residents in primary care in Shanghai, China.

Authors

  • Ning Chen
    Department of General Surgery, Peking University Third Hospital, Beijing, P. R. China.
  • Feng Fan
    School of Medicine, Tongji University, Shanghai, China.
  • Jinsong Geng
    School of Medicine, Nantong University, Nantong, China.
  • Yan Yang
    Department of Endocrinology and Metabolism, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Ya Gao
    BGI-Shenzhen, Shenzhen, China.
  • Hua Jin
    HBI Solutions Inc, Palo Alto, CA, United States.
  • Qiao Chu
    School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Dehua Yu
    Department of General Practice, Yangpu Hospital, Tongji University School of Medicine, Shanghai, China.
  • Zhaoxin Wang
    The First Affiliated Hospital of Hainan Medical University, Haikou, China.
  • Jianwei Shi
    Department of General Practice, Yangpu Hospital, Tongji University School of Medicine, Shanghai, China.

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