Comparing the performance of machine learning and conventional models for predicting atherosclerotic cardiovascular disease in a general Chinese population.

Journal: BMC medical informatics and decision making
PMID: 37488520

Abstract

BACKGROUND: Accurately predicting the risk of atherosclerotic cardiovascular disease (ASCVD) is crucial for implementing individualized prevention strategies and improving patient outcomes. Our objective is to develop machine learning (ML)-based models for predicting ASCVD risk in a prospective Chinese population and compare their performance with conventional regression models.

Authors

  • Zihao Fan
    School of Information, University of California, Berkeley, CA, USA.
  • Zhi Du
    Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
  • Jinrong Fu
    Department of Endocrinology and Metabolism, The First Hospital of China Medical University, No. 155, Nanjing Bei Street, Shenyang, 110001, China.
  • Ying Zhou
    Institute of Drug Metabolism and Pharmaceutical Analysis, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
  • Pengyu Zhang
    School of Software, Shandong University, Jinan, Shandong 250101, China.
  • Chuning Shi
    Department of Cardiology, The First Hospital of China Medical University, No. 155, Nanjing Bei Street, Shenyang, 110001, China.
  • Yingxian Sun
    Department of Cardiology, The First Hospital of China Medical University, No. 155, Nanjing Bei Street, Shenyang, 110001, China. yxsun@cmu.edu.cn.

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